{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","employer_pilot_url":"https://aidevboard.com/verified-interview-pilot","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"candidate_resume_action":{"application_authorized":false,"candidate_charge":0,"endpoint":"https://aidevboard.com/api/v1/candidate/resume-preview","job_id_json_path":"jobs[].id","method":"POST","preview_requires_identity":false,"required_body_fields":["job_id","evidence_bullets"],"requires_explicit_human_review":true,"saved_artifact_protocol":"mcp","saved_artifact_requires_verified_human":true,"saved_artifact_tool":"compile_job_specific_resume","search_requires_identity":false,"status":"available_after_candidate_selects_job","submission_performed":false,"uses_candidate_verified_evidence":true},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"de2fe643-57d2-481c-ae63-e9cde0fbbe4f","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Senior Software Engineer, Production Engineering","slug":"senior-software-engineer-production-engineering-571d6200","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nHarvey is building the AI platform trusted by the world’s leading law firms and enterprises. Our infrastructure is the foundation that powers every customer interaction, every model inference, and every production workload.\n\nWe’re looking for a Production Engineer to help build and operate Harvey’s core compute and networking infrastructure, Kubernetes platform, workflow orchestration platform, and production infrastructure foundations. You’ll work on the systems that enable engineering teams to move quickly and operate reliable services at scale.\n\nIn this role, you’ll improve the reliability, scalability, security, and efficiency of Harvey’s infrastructure platform. You’ll solve complex production challenges across compute fleet management, capacity planning, infrastructure automation, and production operations. You’ll partner closely with Product Engineering, Security, AI Infrastructure, and Platform teams to ensure our infrastructure scales with Harvey’s rapid growth.\n\nAt Harvey, we value Decisiveness, Simplicity, and the belief that Job’s Not Finished. We move quickly, prioritize clarity, and continuously raise the bar for engineering excellence.\n\n\n\n\nWHAT YOU'LL DO\n\n\nINFRASTRUCTURE ENGINEERING \u0026 TECHNICAL LEADERSHIP\n\n - Design, build, and operate the production infrastructure that powers Harvey’s products and AI workloads.\n\n - Drive technical direction across compute infrastructure, networking, Kubernetes, workflow orchestration, and production operations.\n\n - Lead complex, cross-functional technical initiatives that improve reliability, scalability, security, operational efficiency, and infrastructure cost.\n\n - Partner with Product Engineering, Security, AI Infrastructure, and Platform teams to translate product and business requirements into resilient infrastructure solutions.\n\n - Establish reusable patterns, tooling, and paved paths that help engineering teams ship and operate production services safely.\n\n - Raise the engineering bar through thoughtful design reviews, clear technical documentation, operational rigor, and mentorship.\n\n\nINFRASTRUCTURE FOUNDATION \u0026 PRODUCTION OPERATIONS\n\n - Build and operate Harvey’s global compute and network infrastructure, ensuring high availability, scalability, reliability, and performance.\n\n - Improve compute utilization, performance, and service availability while supporting rapidly growing AI workloads.\n\n - Develop capacity models, demand forecasts, and fleet lifecycle automation to help infrastructure scale efficiently with business growth.\n\n - Operate and continuously improve Harvey’s Kubernetes platform, including cluster provisioning, upgrades, networking, monitoring, reliability, performance, and operational automation.\n\n - Drive infrastructure cost efficiency through capacity management, resource rightsizing, workload optimization, and utilization monitoring.\n\n - Build secure infrastructure foundations, including identity and access management, network isolation, secrets management, auditing, and compliance controls.\n\n - Develop scalable Infrastructure-as-Code and automation frameworks using technologies such as Terraform and Pulumi.\n\n - Improve observability, monitoring, alerting, incident response, and operational readiness across the infrastructure platform.\n\n - Participate in the on-call rotation, lead incident response when needed, and turn production learnings into durable engineering improvements.\n\n\n\n\nWHAT YOU HAVE\n\n - 5+ years of software, infrastructure, site reliability, or production engineering experience.\n\n - Deep experience building and operating large-scale cloud infrastructure on AWS, Azure, or Google Cloud Platform.\n\n - Strong hands-on experience operating Kubernetes in production, including ","salary_min":161300,"salary_max":241900,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","llm","distributed-systems","agents"],"apply_url":"https://jobs.ashbyhq.com/harvey/dbd9a156-a841-42a0-a3e5-beed8de60a7c/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T23:37:30.751Z","expires_at":"2026-08-30T14:02:51.494115Z","created_at":"2026-07-31T14:02:51.633596Z","updated_at":"2026-07-31T14:02:51.633596Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/de2fe643-57d2-481c-ae63-e9cde0fbbe4f"},{"id":"7cdce99e-031e-43cb-9967-7cec462f2c5b","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Staff Software Engineer, Production Engineering","slug":"staff-software-engineer-production-engineering-491a96a6","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nHarvey is building the AI platform trusted by the world’s leading law firms and enterprises. Our infrastructure is the foundation that powers every customer interaction, every model inference, and every production workload.\n\nWe’re looking for a Production Engineer to help build and operate Harvey’s core compute and networking infrastructure, Kubernetes platform, workflow orchestration platform, and production infrastructure foundations. You’ll work on the systems that enable engineering teams to move quickly and operate reliable services at scale.\n\nIn this role, you’ll improve the reliability, scalability, security, and efficiency of Harvey’s infrastructure platform. You’ll solve complex production challenges across compute fleet management, capacity planning, infrastructure automation, and production operations. You’ll partner closely with Product Engineering, Security, AI Infrastructure, and Platform teams to ensure our infrastructure scales with Harvey’s rapid growth.\n\nAt Harvey, we value Decisiveness, Simplicity, and the belief that Job’s Not Finished. We move quickly, prioritize clarity, and continuously raise the bar for engineering excellence.\n\n\n\n\nWHAT YOU'LL DO\n\n\nINFRASTRUCTURE ENGINEERING \u0026 TECHNICAL LEADERSHIP\n\n - Design, build, and operate the production infrastructure that powers Harvey’s products and AI workloads.\n\n - Drive technical direction across compute infrastructure, networking, Kubernetes, workflow orchestration, and production operations.\n\n - Lead complex, cross-functional technical initiatives that improve reliability, scalability, security, operational efficiency, and infrastructure cost.\n\n - Partner with Product Engineering, Security, AI Infrastructure, and Platform teams to translate product and business requirements into resilient infrastructure solutions.\n\n - Establish reusable patterns, tooling, and paved paths that help engineering teams ship and operate production services safely.\n\n - Raise the engineering bar through thoughtful design reviews, clear technical documentation, operational rigor, and mentorship.\n\n\nINFRASTRUCTURE FOUNDATION \u0026 PRODUCTION OPERATIONS\n\n - Build and operate Harvey’s global compute and network infrastructure, ensuring high availability, scalability, reliability, and performance.\n\n - Improve compute utilization, performance, and service availability while supporting rapidly growing AI workloads.\n\n - Develop capacity models, demand forecasts, and fleet lifecycle automation to help infrastructure scale efficiently with business growth.\n\n - Operate and continuously improve Harvey’s Kubernetes platform, including cluster provisioning, upgrades, networking, monitoring, reliability, performance, and operational automation.\n\n - Drive infrastructure cost efficiency through capacity management, resource rightsizing, workload optimization, and utilization monitoring.\n\n - Build secure infrastructure foundations, including identity and access management, network isolation, secrets management, auditing, and compliance controls.\n\n - Develop scalable Infrastructure-as-Code and automation frameworks using technologies such as Terraform and Pulumi.\n\n - Improve observability, monitoring, alerting, incident response, and operational readiness across the infrastructure platform.\n\n - Participate in the on-call rotation, lead incident response when needed, and turn production learnings into durable engineering improvements.\n\n\n\n\nWHAT YOU HAVE\n\n - 10+ years of software, infrastructure, site reliability, or production engineering experience.\n\n - Deep experience building and operating large-scale cloud infrastructure on AWS, Azure, or Google Cloud Platform.\n\n - Strong hands-on experience operating Kubernetes in production, including","salary_min":231000,"salary_max":340000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","cloud","llm","distributed-systems"],"apply_url":"https://jobs.ashbyhq.com/harvey/11571c63-2fc5-41ce-b855-c949ead5efca/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T23:37:06.693Z","expires_at":"2026-08-30T14:02:51.578555Z","created_at":"2026-07-31T14:02:51.787171Z","updated_at":"2026-07-31T14:02:51.787171Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7cdce99e-031e-43cb-9967-7cec462f2c5b"},{"id":"c34cb4dc-e213-4e56-8a8c-21698bf37856","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Staff Software Engineer, Production Engineering","slug":"staff-software-engineer-production-engineering-3d8da43c","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nHarvey is building the AI platform trusted by the world’s leading law firms and enterprises. Our infrastructure is the foundation that powers every customer interaction, every model inference, and every production workload.\n\nWe’re looking for a Production Engineer to help build and operate Harvey’s core compute and networking infrastructure, Kubernetes platform, workflow orchestration platform, and production infrastructure foundations. You’ll work on the systems that enable engineering teams to move quickly and operate reliable services at scale.\n\nIn this role, you’ll improve the reliability, scalability, security, and efficiency of Harvey’s infrastructure platform. You’ll solve complex production challenges across compute fleet management, capacity planning, infrastructure automation, and production operations. You’ll partner closely with Product Engineering, Security, AI Infrastructure, and Platform teams to ensure our infrastructure scales with Harvey’s rapid growth.\n\nAt Harvey, we value Decisiveness, Simplicity, and the belief that Job’s Not Finished. We move quickly, prioritize clarity, and continuously raise the bar for engineering excellence.\n\n\n\n\nWHAT YOU'LL DO\n\n\nINFRASTRUCTURE ENGINEERING \u0026 TECHNICAL LEADERSHIP\n\n - Design, build, and operate the production infrastructure that powers Harvey’s products and AI workloads.\n\n - Drive technical direction across compute infrastructure, networking, Kubernetes, workflow orchestration, and production operations.\n\n - Lead complex, cross-functional technical initiatives that improve reliability, scalability, security, operational efficiency, and infrastructure cost.\n\n - Partner with Product Engineering, Security, AI Infrastructure, and Platform teams to translate product and business requirements into resilient infrastructure solutions.\n\n - Establish reusable patterns, tooling, and paved paths that help engineering teams ship and operate production services safely.\n\n - Raise the engineering bar through thoughtful design reviews, clear technical documentation, operational rigor, and mentorship.\n\n\nINFRASTRUCTURE FOUNDATION \u0026 PRODUCTION OPERATIONS\n\n - Build and operate Harvey’s global compute and network infrastructure, ensuring high availability, scalability, reliability, and performance.\n\n - Improve compute utilization, performance, and service availability while supporting rapidly growing AI workloads.\n\n - Develop capacity models, demand forecasts, and fleet lifecycle automation to help infrastructure scale efficiently with business growth.\n\n - Operate and continuously improve Harvey’s Kubernetes platform, including cluster provisioning, upgrades, networking, monitoring, reliability, performance, and operational automation.\n\n - Drive infrastructure cost efficiency through capacity management, resource rightsizing, workload optimization, and utilization monitoring.\n\n - Build secure infrastructure foundations, including identity and access management, network isolation, secrets management, auditing, and compliance controls.\n\n - Develop scalable Infrastructure-as-Code and automation frameworks using technologies such as Terraform and Pulumi.\n\n - Improve observability, monitoring, alerting, incident response, and operational readiness across the infrastructure platform.\n\n - Participate in the on-call rotation, lead incident response when needed, and turn production learnings into durable engineering improvements.\n\n\n\n\nWHAT YOU HAVE\n\n - 10+ years of software, infrastructure, site reliability, or production engineering experience.\n\n - Deep experience building and operating large-scale cloud infrastructure on AWS, Azure, or Google Cloud Platform.\n\n - Strong hands-on experience operating Kubernetes in production, including","salary_min":231000,"salary_max":340000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","distributed-systems","llm","cloud"],"apply_url":"https://jobs.ashbyhq.com/harvey/dad3437f-9f4c-444a-bf4a-ae2a9a1047c2/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T23:36:49.147Z","expires_at":"2026-08-30T14:02:51.732912Z","created_at":"2026-07-31T14:02:51.873377Z","updated_at":"2026-07-31T14:02:51.873377Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c34cb4dc-e213-4e56-8a8c-21698bf37856"},{"id":"f6b450cf-09f3-4a6a-8009-3581532effd1","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Senior Software Engineer, Production Engineering","slug":"senior-software-engineer-production-engineering-c605fa70","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nHarvey is building the AI platform trusted by the world’s leading law firms and enterprises. Our infrastructure is the foundation that powers every customer interaction, every model inference, and every production workload.\n\nWe’re looking for a Production Engineer to help build and operate Harvey’s core compute and networking infrastructure, Kubernetes platform, workflow orchestration platform, and production infrastructure foundations. You’ll work on the systems that enable engineering teams to move quickly and operate reliable services at scale.\n\nIn this role, you’ll improve the reliability, scalability, security, and efficiency of Harvey’s infrastructure platform. You’ll solve complex production challenges across compute fleet management, capacity planning, infrastructure automation, and production operations. You’ll partner closely with Product Engineering, Security, AI Infrastructure, and Platform teams to ensure our infrastructure scales with Harvey’s rapid growth.\n\nAt Harvey, we value Decisiveness, Simplicity, and the belief that Job’s Not Finished. We move quickly, prioritize clarity, and continuously raise the bar for engineering excellence.\n\n\n\n\nWHAT YOU'LL DO\n\n\nINFRASTRUCTURE ENGINEERING \u0026 TECHNICAL LEADERSHIP\n\n - Design, build, and operate the production infrastructure that powers Harvey’s products and AI workloads.\n\n - Drive technical direction across compute infrastructure, networking, Kubernetes, workflow orchestration, and production operations.\n\n - Lead complex, cross-functional technical initiatives that improve reliability, scalability, security, operational efficiency, and infrastructure cost.\n\n - Partner with Product Engineering, Security, AI Infrastructure, and Platform teams to translate product and business requirements into resilient infrastructure solutions.\n\n - Establish reusable patterns, tooling, and paved paths that help engineering teams ship and operate production services safely.\n\n - Raise the engineering bar through thoughtful design reviews, clear technical documentation, operational rigor, and mentorship.\n\n\nINFRASTRUCTURE FOUNDATION \u0026 PRODUCTION OPERATIONS\n\n - Build and operate Harvey’s global compute and network infrastructure, ensuring high availability, scalability, reliability, and performance.\n\n - Improve compute utilization, performance, and service availability while supporting rapidly growing AI workloads.\n\n - Develop capacity models, demand forecasts, and fleet lifecycle automation to help infrastructure scale efficiently with business growth.\n\n - Operate and continuously improve Harvey’s Kubernetes platform, including cluster provisioning, upgrades, networking, monitoring, reliability, performance, and operational automation.\n\n - Drive infrastructure cost efficiency through capacity management, resource rightsizing, workload optimization, and utilization monitoring.\n\n - Build secure infrastructure foundations, including identity and access management, network isolation, secrets management, auditing, and compliance controls.\n\n - Develop scalable Infrastructure-as-Code and automation frameworks using technologies such as Terraform and Pulumi.\n\n - Improve observability, monitoring, alerting, incident response, and operational readiness across the infrastructure platform.\n\n - Participate in the on-call rotation, lead incident response when needed, and turn production learnings into durable engineering improvements.\n\n\n\n\nWHAT YOU HAVE\n\n - 5+ years of software, infrastructure, site reliability, or production engineering experience.\n\n - Deep experience building and operating large-scale cloud infrastructure on AWS, Azure, or Google Cloud Platform.\n\n - Strong hands-on experience operating Kubernetes in production, including ","salary_min":161300,"salary_max":241900,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","distributed-systems","agents","cloud"],"apply_url":"https://jobs.ashbyhq.com/harvey/6a840e0d-6af8-4ad6-a182-7534ce64e7e2/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T23:36:32.93Z","expires_at":"2026-08-30T14:02:51.372468Z","created_at":"2026-07-31T14:02:51.549594Z","updated_at":"2026-07-31T14:02:51.549594Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f6b450cf-09f3-4a6a-8009-3581532effd1"},{"id":"779deee1-0368-49f3-ac2e-15d6f3fab503","company_id":"1f4520df-9fc1-4ace-a80b-6c3266f03e8a","title":"Governance, Risk and Compliance Lead","slug":"governance-risk-and-compliance-lead-c6e10c7d","description":"Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals. \n We are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.\n About the Role \n We're looking for a GRC Lead who personally drives our certifications (SOC 2, ISO 27001, FedRAMP and others as we grow) from scoping through audit close, and runs our compliance processes day to day. You'll collect the evidence, write the control documentation, and sit across from the auditor yourself.\n You'll work closely with security, legal, safety, and engineering to answer compliance and risk questions directly, using your own technical understanding of how our systems work. Day to day, you'll be managing audits, controls, and risk assessments. Alongside that, you'll be building the roadmap for what this function needs to look like in a year.\n What You'll Do \n \n Own our certification roadmap end to end: scope each certification, build the control set, collect and organize evidence, and represent TML directly to auditors through to close.\n Manage recurring compliance processes on a set cadence: control testing, audit prep and response, risk register maintenance, and policy attestations.\n Answer compliance and risk questions from engineering, security, and product teams directly, by building enough technical fluency across our infrastructure, model deployment, and data handling to do so without escalating every question.\n Track regulatory and framework requirements relevant to an AI company (GDPR, EU AI Act, and similar) and translate them into specific, actionable controls.\n Identify gaps in current compliance coverage as the company adds new products, infrastructure, or jurisdictions, and propose what needs to change before it becomes a blocker.\n Build and maintain the tooling and documentation that make the next audit cycle faster than the last one.\n Plan a multi-quarter roadmap for the GRC function itself, while continuing to personally run the certifications and audits already on the books.\n \n Skills and Qualifications \n Minimum qualifications: \n \n 7+ years related experience across technology and cybersecurity Governance, Risk, and Compliance (GRC), with demonstrated breadth across all three disciplines.\n Experience leading a SOC 2, ISO 27001, FedRAMP or comparable certification from scoping through audit close.\n Hands-on experience collecting audit evidence and writing control documentation.\n Experience managing a recurring compliance process, such as control testing, risk register maintenance, or policy attestations.\n Experience learning new technical domains quickly and translating them for non-technical stakeholders.\n \n Preferred qualifications: \n We encourage you to apply even if you don't meet all preferred qualifications. \n \n Background as a software engineer or in a technical engineering role, now applied to GRC, evidenced by scripts, tools, or automations you've personally built for evidence collection, control testing, or audit workflows.\n Experience translating complex compliance requirements into scalable automation using AI agents and custom built tooling.\n Experience growing a GRC function's capability (new certifications, tooling, or processes) as a company scaled.\n \n You'll Thrive in This Role if \n \n You want to run the certification yourself, end to end.\n You're the one in the room with the auditor, walking through evidence.\n You can hold this week's deadlines and next year's roadmap at the same time.\n \n Logistics \n \n Location: This role is based in San Francisco, California.\n Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $225,000 - $350,000.\n Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.\n Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.\n As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.\n As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law. \n Thinking Machines Lab will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.","salary_min":225000,"salary_max":350000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","pytorch","mlops","security"],"apply_url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5375726008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T19:58:17Z","expires_at":"2026-08-30T14:18:47.01745Z","created_at":"2026-07-31T14:18:47.119503Z","updated_at":"2026-07-31T14:18:47.119503Z","company_name":"Thinking Machines","company_slug":"thinking-machines","company_logo_url":"https://www.google.com/s2/favicons?domain=thinkingmachin.es\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/779deee1-0368-49f3-ac2e-15d6f3fab503"},{"id":"7e2ff3bd-ddcd-46de-9de6-f861ceb0ca00","company_id":"fa25a1f6-acd0-42b6-a229-f4d258ed5c3d","title":"Senior Data Scientist","slug":"senior-data-scientist-e96948ff","description":"Employee Applicant Privacy Notice \n Who we are: \n \n Shape a brighter financial future with us.\n Together with our members, we’re changing the way people think about and interact with personal finance.\n We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world. \n The Role: \n The Risk Data Science team is looking for a Senior Data Scientist to develop advanced machine learning and statistical models, guide measurement, strategy, and data-driven decision making to support various credit risk and operational areas at SoFi. The Data Scientist will work closely with Credit, Risk, Product, Engineering, and Operations teams to design solutions for underwriting, portfolio management, loss mitigation, and loss forecasting etc. These tasks involve researching and applying state of the art modeling methodologies to solve complex business problems. This role is very rewarding as your work will have a direct and immediate impact on the business’ profitability.\n What You’ll Do: \n \n Develop, implement, and continuously improve machine learning and statistical models that support various credit, risk, and operational procedures including but not limited to underwriting, portfolio management, loss mitigation, and loss forecasting, etc.\n Present model performance and insights to Credit, Risk, and Business Unit leaders.\n Proactively identify opportunities to apply advanced modeling approaches to solve complex business problems.\n Explore and leverage in-house and external data sources to enhance model predictive power.\n Collaborate with the Model Risk Management team to demonstrate models are developed with high level rigor that satisfy Model Risk Management and Governance requirements. \n Perform ongoing monitoring of the models through the construction of dashboards and KPI tracking\n Collaborate with the Product and Engineering teams to improve the model development, deployment, monitoring, and model re-calibration/re-build process..\n Explore and apply in-house and open-source machine learning and statistical tools and algorithms to develop and improve models.\n \n What You’ll Need: \n \n Master’s degree in Statistics, Econometrics, Mathematics, Operations Research, Physics, Computer Science, Engineering, or quantitative field required. PhD degree preferred.\n 3+ years of relevant work experience in building and implementing machine learning and statistical models.\n Excellent logic reasoning and communication abilities when interpreting business requirements and translating them into effective data solutions.\n Strong skills in writing efficient SQL queries and Python code to create complex attributes, especially with large datasets.\n Strong sensitivity to details in data and proactively investigate them to uncover unknown patterns.\n Strong knowledge of databases and related languages/tools such as SQL, NoSQL, Hive, etc. \n Demonstrated sophisticated experience in building efficient and reliable pipelines that interact with large datasets stored in SageMaker and Snowflake, automating recurring processes such as data extraction and processing, feature selection, model training, model monitoring, and generating documentation templates to support reproducibility and cross-functional collaboration.\n Excellent knowledge of machine learning and statistical modeling methods for supervised and unsupervised learning. These methods include (but are not limited to) regression, classification, clustering, outlier detection, novelty detection, decision trees, nearest neighbors, support vector machines, ensemble methods and boosting, neural networks, deep learning and its various applications. Continuously following the advancement of machine learning and artificial intelligence to update your knowledge and skills in order to solve business problems with the most efficient methodologies\n Strong programming skills in Python and machine learning libraries (e.g., sklearn, lightgbm, xgboost, pytorch, tensorflow, keras, etc.)\n \n Nice To Have: \n \n Experience in a lending organization.\n Experience with model documentation and delivering effective verbal and written communication.\n Experience in working closely with Product, Engineering, and Model Risk Management teams.\n Experience with AWS or GCP.\n \n Compensation and Benefits \n The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location. \n  \n This role may also be eligible for a bonus and/or long term incentives. Your recruiter will provide more information","salary_min":128000,"salary_max":180000,"location":"Frisco, TX","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","deep-learning","pytorch","tensorflow","mlops","data-science"],"apply_url":"https://sofi.com/careers/job/7819499003?gh_jid=7819499003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T19:28:51Z","expires_at":"2026-08-30T14:19:37.336478Z","created_at":"2026-07-31T14:19:37.432523Z","updated_at":"2026-07-31T14:19:37.432523Z","company_name":"SoFi","company_slug":"sofi","company_logo_url":"https://www.google.com/s2/favicons?domain=sofi.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7e2ff3bd-ddcd-46de-9de6-f861ceb0ca00"},{"id":"729bdc61-a693-49f1-bc55-87988031946c","company_id":"3029e985-56bf-4ac2-9ae1-df4cdd53b12f","title":"Principal, AI Research Engineering","slug":"principal-ai-research-engineering-e33034b4","description":"About Zscaler \n Zscaler accelerates digital transformation to ensure our customers can be more agile, efficient, resilient, and secure. As an AI-forward enterprise , we are constantly pushing the envelope, leveraging the world’s largest security data lake to power our cloud-native Zero Trust Exchange platform. This innovation protects our customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location.\n Here, impact in your role matters more than title and trust is built on results. We say, impact over activity. We seek innovators who actively use AI to amplify their impact and who thrive in an environment where we leverage intelligent systems to stay ahead of evolving threats. We believe in transparency and value constructive, honest debate —we’re focused on getting to the best ideas, faster. We build high-performing teams that can make an impact quickly and with high quality. To do this, we are building a culture of execution centered on customer obsession , collaboration, ownership, and accountability.\n We value high-impact, high-accountability with a sense of urgency where you’re enabled to do your best work and embrace your potential. If you’re driven by purpose, thrive on solving complex challenges, and want to be part of the team that’s helping to secure the AI age, we invite you to bring your talents to Zscaler and help shape the future of cybersecurity.\n Role \n We are looking for a Principal, AI Research Engineer to join our AI \u0026 Data Protection team. This role is hybrid based in San Jose, CA, and reports to the EVP, AI Security and Strategic Initiatives.\n Zscaler’s mission is to secure the world’s data in the age of AI. As the leader of the world’s largest security cloud, the Zero Trust Exchange, we are uniquely positioned to define how the global enterprise safely adopts, scales, and secures Artificial Intelligence. You will be the primary architect of Zscaler’s AI ecosystem, bridging the gap between our core Zero Trust platform and the rapidly evolving world of LLMs, GPU infrastructure, and AI application frameworks. This role is ideal for a technical leader who deeply understands the \"AI Stack\" and has a proven track record of building thriving developer and partner ecosystems at scale.\n What you’ll do (Role Expectations) \n \n Define the AI Ecosystem Strategy by leading the vision for integrations with frontier model providers, vector database companies, and AI infrastructure leaders\n Lead the \"Secure AI\" roadmap by owning the lifecycle for features that enable safe use of 3rd-party LLMs and prevent data leakage\n Build a developer ecosystem by defining and launching APIs, SDKs, and integration patterns for \"Zero Trust-native\" applications\n Drive market leadership and cross-functional partnerships to turn complex technical integrations into high-growth go-to-market motions\n \n Who You Are (Success Profile) \n \n You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.\n You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.\n You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.\n You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.\n You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.\n \n What We’re Looking for (Minimum Qualifications) \n \n 8+ years of experience in technical leadership or engineering partnerships with a history of building impactful industry relationships\n Deep technical fluency in the AI lifecycle including training vs. inference, GPU roles, context windows, and RAG architectures\n Ability to apply first-principles thinking to fundamental security and networking challenges in enterprise LLM deployment\n Strong bias for action with a \"Ship Early, Iterate Fast\" mentality and focus on delivering high-value MVPs\n Foundational understanding of AI/ML technologies and experience leveraging, securing, or positioning AI-driven solutions to optimize outcomes within your functional domain   \n \n What Will Make You Stand Out (Preferred Qualifications) \n \n Proven track record of building thriving developer and partner ecosyst","salary_min":171500,"salary_max":245000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["data-pipeline","gpu","llm","rag","embeddings","security","search","research"],"apply_url":"https://job-boards.greenhouse.io/zscaler/jobs/5058767007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T18:11:08Z","expires_at":"2026-08-30T14:10:21.783546Z","created_at":"2026-07-31T14:10:21.914294Z","updated_at":"2026-07-31T14:10:21.914294Z","company_name":"Zscaler","company_slug":"zscaler","company_logo_url":"https://www.google.com/s2/favicons?domain=zscaler.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/729bdc61-a693-49f1-bc55-87988031946c"},{"id":"f58aea8d-a306-4d60-bd12-166357332a58","company_id":"b459414f-fd43-42c4-a6e1-f07225286a75","title":"Infrastructure Engineer - Member of Technical Staff","slug":"infrastructure-engineer-member-of-technical-staff-4ef83d2d","description":"ABOUT THE COMPANY\n\nSimile is The Simulation Company. We simulate human behavior to keep people at the center of the decisions that shape the world. With AI, anyone can create a product, a campaign, a policy, or a script — the bottleneck has moved upstream. The hard question is no longer whether you can create something, but what to create, for whom, and how to bring it to life. Those are fundamentally human decisions, and they shouldn't be left to chance or handed off to an algorithm. We're building the infrastructure to understand human behavior at scale and to represent humans in an increasingly agentic world. Our mission is to simulate all eight billion people on earth.\n\n\n\nWe launched five months ago. Since then we've grown revenue 5x, built a new foundation model for human behavior that has run tens of millions of simulations for F100 enterprises, trained a first-of-its-kind confidence model that predicts the accuracy of every simulation, and released the first product that lets organizations verifiably predict the future. The world's leading companies use Simile to make business-critical decisions — from consumer leaders like CVS Health and Wealthfront to professional services organizations like Deloitte and Gallup — strategizing product launches, entering new markets, and forecasting earnings calls.\n\n\n\nWe've raised over $200M at a $2B post-money valuation led by Greenoaks, with Index Ventures, Hanabi, A*, Bain Capital Ventures, and CVS Health Ventures. We've grown from a small home in Palo Alto to a global team of 50+, and we're building a team of the best researchers, engineers, designers, and operators in the world. The future is too important to be left to chance.\n\n\n\n\n\nABOUT THE TEAM\n\nThe Infrastructure team is the backbone of our platform. We build the foundational systems that allow our AI agents to operate at scale with uncompromising security. We operate at the intersection of high-scale cloud networking, distributed systems, and enterprise-grade privacy.\n\n\n\n\nWE ORGANIZE OUR WORK INTO THREE CORE PILLARS:\n\n - Cloud Foundation: Managing our multi-cloud footprint (AWS/GCP) with a focus on high availability, cost-efficiency, and Infrastructure-as-Code.\n\n - Enterprise Deployments: Building the \"paved paths\" for VPC peering, PrivateLink, and BYOC (Bring Your Own Cloud) architectures for our largest customers.\n\n - Platform \u0026 Reliability: Developing the CI/CD pipelines and observability stacks (p99 latency tracking, SLOs) that empower our entire engineering org to ship safely.\n\n\n\n\nABOUT THE ROLE\n\nWe are looking for an Infrastructure Engineer who thrives on the complexity of modern deployment patterns. You will own the infrastructure roadmap from design to operation, ensuring our platform is resilient, compliant, and ready for global scale.\n\n\n\n\nRESPONSIBILITIES\n\n - Architect Multi-Cloud Environments: Design and scale multi-region architectures across AWS and GCP to support global data residency and failover requirements.\n\n - Enable Engineering Velocity: Partner cross-functionally with Product Engineering, Research, and Security teams to build internal tooling and \"paved paths\" that accelerate development velocity and empower every engineer to ship with confidence.\n\n - Own Enterprise Connectivity: Build and automate secure networking solutions, including VPC peering, PrivateLink, and dedicated interconnects for customer-managed environments.\n\n - Drive Reliability: Set and maintain strict SLOs. You’ll optimize networking paths and resource allocation to ensure our real-time AI features hit their latency targets.\n\n - Champion GitOps: Manage our entire stack via Terraform/Pulumi; ensuring that \"the code is the truth\" across all environments.\n\n - Security \u0026 Compliance: Implement \"security-by-design,\" focusing on encryption at rest/transit and identity management (SAML/SCIM) to meet SOC2 and HIPAA standards.\n\n\n\n\nREQUIREMENTS\n\n\nMUST HAVES\n\n - 5+ years of experience building production-grade infrastructure in a high-growth environment.\n\n - Cloud Polyglot: Deep expertise in AWS is required; experience with GCP or Azure is a major plus.\n\n - Networking Guru: Deep understanding of DNS, Load Balancing, Service Meshes, and complex VPC routing.\n\n - IaC Architect: Proven track record managing large-scale environments using Terraform, Pulumi, or other production-ready, battle-tested IaC tools with a focus on reusable, modular infrastructure.\n\n - Operational Mindset: Experience with modern observability (Datadog, OpenTelemetry) and a \"you build it, you run it\" mentality.\n\n - Communication: Ability to write clear technical specs for both internal teams and external customers.\n\n\nNICE TO HAVES\n\n - AI/ML Infrastructure: Experience building or scaling infrastructure for AI/ML workloads, specifically high-throughput inference systems or GPU-accelerated computing.\n\n - Kubernetes Mastery: Strong K8s (EKS/GKE) experience, specifically around multi-tenant security and resource isolation.\n \n\n\n\n\nCOMPENSATION \u0026 BENEFITS\n\nAt","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","distributed-systems","agents","cloud","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/simile/ed518612-bd1e-416b-9e53-77f363eae525/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T17:16:03.287Z","expires_at":"2026-08-30T14:11:51.540988Z","created_at":"2026-04-14T03:21:23.993958Z","updated_at":"2026-07-31T14:11:51.666253Z","company_name":"Simile","company_slug":"simile","company_logo_url":"https://www.google.com/s2/favicons?domain=simile.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f58aea8d-a306-4d60-bd12-166357332a58"},{"id":"426da38a-255b-4b18-aa49-edd706e59a31","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Research Engineer, Large-Scale Training","slug":"research-engineer-large-scale-training-19a25c49","description":"About the Role \n The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems. \n As a Research Engineer on the Scaling Team within Model Shaping, you will turn cutting-edge research on efficient foundation model training into robust, high-performance systems. You will profile and optimize Together's training infrastructure, identify performance bottlenecks across the stack, and implement state-of-the-art techniques from both the research literature and our own scientists in production environments. \n Your work will directly shape the fine-tuning experience of Together's customers. You will rapidly bring newly released open-source models onto the Model Shaping platform, ensuring they train efficiently and reliably across diverse customer workloads. Working closely with Research Scientists, you will also build the experimental infrastructure that accelerates research and enables validated ideas to be deployed reliably at scale. \n Responsibilities \n \n Design, implement, and optimize core components of Together's large-scale training infrastructure. \n Integrate new model architectures, validate training correctness and convergence, and optimize performance for production fine-tuning workloads. \n Profile distributed training workloads to identify and eliminate bottlenecks across compute, memory, and communication. \n Design and execute experiments to validate performance hypotheses and benchmark new approaches against state-of-the-art methods. \n Partner closely with Research Scientists to productionize novel training methods and contribute to publications and open-source releases. \n Rapidly enable support for newly released open-source foundation models on the Together platform. \n Build and maintain experimental infrastructure that accelerates research while ensuring production-quality reliability and scalability. \n \n Requirements \n \n Demonstrated ability to independently take ambiguous performance or infrastructure problems from investigation through deployment. \n Strong programming skills in Python and PyTorch, with an emphasis on writing efficient, maintainable code. \n Hands-on experience training or fine-tuning large neural networks in multi-GPU or multi-node environments. \n Solid understanding of ML systems fundamentals, including GPU architecture, mixed-precision training, and distributed training paradigms such as data, tensor, pipeline, or expert parallelism. \n Strong communication skills and the ability to collaborate effectively with both researchers and engineers. \n Passion for staying current with advances in AI research and applying them to real-world systems. \n Excitement about translating cutting-edge research into production systems that deliver customer impact. \n \n Nice to Have \n \n Experience writing optimized NVIDIA GPU kernels using CUDA or Triton, or implementing communication collectives with technologies such as NCCL or NVSHMEM. \n Experience with large-scale training frameworks such as FSDP, DeepSpeed, Megatron-LM, or custom distributed training systems. \n Experience optimizing distributed training for compute efficiency, memory efficiency, or scalability. \n Experience running and managing large-scale GPU experiments, including scheduling, monitoring, and fault tolerance. \n Contributions to widely used open-source ML or ML systems projects. \n Experience building or operating ML products or managed services used by external customers. \n \n About Together AI \n Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, ATLAS, RedPajama, and Mamba. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure. \n Compensation \n We offer competitive compensation, startup equity, health insurance, and other benefits. The US base salary range for this full-time position is $200,000 - $290,000. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. \n Equal Opportunity \n Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone reg","salary_min":200000,"salary_max":290000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["nlp","gpu","generative-ai","pytorch","deep-learning","fine-tuning","distributed-systems","search"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5199554007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T16:52:05Z","expires_at":"2026-08-30T14:02:19.764671Z","created_at":"2026-07-31T14:02:19.892066Z","updated_at":"2026-07-31T14:02:19.892066Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/426da38a-255b-4b18-aa49-edd706e59a31"},{"id":"fd3eb5ae-4e46-413b-8f93-f36fa985d12b","company_id":"5d6de1f6-4d6c-463b-8a2b-a5caeadb97b4","title":"Senior Software Engineer, Airflow Infrastructure - NYC","slug":"senior-software-engineer-airflow-infrastructure-nyc-56b64bea","description":"Astronomer empowers data teams to bring mission-critical software, analytics, and AI to life and is the company behind Astro, the industry-leading unified DataOps platform powered by Apache Airflow®. Astro accelerates building reliable data products that unlock insights, unleash AI value, and powers data-driven applications. Trusted by more than 800 of the world's leading enterprises, Astronomer lets businesses do more with their data. To learn more, visit www.astronomer.io http://www.astronomer.io.\n\n\n\n\nABOUT THIS ROLE:\n\nAt Astronomer, we’re redefining how companies run Apache Airflow at scale. Our R\u0026D organization is home to some of the most innovative minds in cloud infrastructure and open-source software.\n\nWe’re looking for a Senior Software Engineer to join our Airflow Infra team, part of Astro, our flagship cloud platform. You’ll be building the critical layer that connects the open-source Airflow ecosystem to enterprise-grade, massively scalable cloud infrastructure. Your work will directly influence how global organizations orchestrate data pipelines at scale—making them faster, more reliable, and easier to manage.\n\nIf you’re driven by impact, excited by scale, and ready to work on the kind of infrastructure challenges that push the boundaries of what’s possible in cloud-native systems, this is the opportunity you’ve been waiting for.\n\n\n\nHybrid Work Model: For this role, you will embrace a flexible hybrid work model with at least 3 days per week in our New York City office.\n\n\n\n\n\n\nWHAT YOU GET TO DO:\n\n - Engineer backend services with high quality, maintainable and well tested code.\n\n - Partner with other engineers, product, customer reliability support, and leadership to achieve business goals and define how our systems should evolve.\n\n - Regularly engage in code reviews and provide constructive feedback.\n\n - Optimize the performance, reliability and scalability of existing backend services.\n\n - Investigate, prototype and propose ideas to improve user experience.\n\n - Create and maintain technical documentation for systems and processes, ensuring clarity and accessibility.\n\n - Participate in on-call rotation, troubleshoot and debug to solve incidents.\n\n\n\n\nWHAT YOU BRING TO THE ROLE:\n\n - 5+ years of experience building and delivering SaaS products.\n\n - Strong proficiency in Python or Golang.\n\n - Hands-on experience with Kubernetes.\n\n - Solid understanding of and experience with integrating with RESTful APIs and distributed systems.\n\n - Comfortable with testing frameworks, such as pytest.\n\n - Strong communication skills, both written and verbal, with experience in creating technical specifications.\n\n - A passion for reliability and operational excellence.\n\n - Ability to scope work and coordinate cross-functionally to address risks and ensure successful delivery.\n\n - Experience with software development best practices, such as code reviews, testing, CI/CD, version control, automation and debugging.\n\n - Ability to adjust to change and rapid pace of development.\n\n - Proactive approach to identifying and addressing issues, with a focus on ownership and accountability.\n\n\n\n\nBONUS POINTS IF YOU HAVE:\n\n - Experience with Apache Airflow\n\n\n\nThe estimated salary for this role ranges from $200,000 - $230,000 based on leveling and geography, along with an equity component and a comprehensive benefits package. This range is merely an estimate; actual compensation may deviate from this range based on skills, experience, and qualifications.\n\n\n\n#LI-Fulltime\n\n#LI-Hybrid\n\n\n\nAt Astronomer, we value diversity. We are an equal opportunity employer: we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.","salary_min":200000,"salary_max":230000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","data-pipeline","cloud","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/astronomer/0166149d-ce80-4a1b-8371-5c989b28c1e7/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T12:55:18.027Z","expires_at":"2026-08-30T14:18:05.658808Z","created_at":"2026-07-30T14:18:05.190927Z","updated_at":"2026-07-31T14:18:05.764215Z","company_name":"Astronomer","company_slug":"astronomer","company_logo_url":"https://www.google.com/s2/favicons?domain=astronomer.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fd3eb5ae-4e46-413b-8f93-f36fa985d12b"},{"id":"c2f24669-9a25-4737-a0de-fe1453d33f0d","company_id":"c587b06c-b6f0-4d1d-b694-6fb6abc2a6bb","title":"Director of Customer Experience","slug":"director-of-customer-experience-82241238","description":"Who We Are \n Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.\n Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.\n We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.\n The Way We Work\n The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:\n \n Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.\n Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.\n Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.\n Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.\n Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.\n Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.\n \n  \n Who We Look For \n The people who thrive at Lightning AI are builders who move with urgency, communicate openly, take ownership, and continuously raise the bar for themselves and their teams.\n We value leaders who create clarity in complex environments, stay close to customers and the work, and build systems that scale without introducing unnecessary processes.\n About the Role \n We are looking for a Director of Customer Experience to build and lead the organization responsible for helping Lightning AI customers realize meaningful value from our platform.\n You will own the strategy, operating model, and execution of our customer experience function, leading a team of Technical Account Managers and Customer Experience Managers supporting customers across the AI lifecycle—from initial onboarding and workload migration through production adoption, optimization, and expansion.\n This role sits at the intersection of customers, engineering, product, sales, and infrastructure. You will be accountable for ensuring customers can successfully build and operate AI workloads on Lightning AI while receiving clear, proactive, and technically credible guidance throughout the relationship.\n The right leader will combine strong customer judgment with technical depth and operational rigor. You should be comfortable getting into the technical details enough so that you understand where customers are blocked, influencing product priorities, and advising executives on retention, risk, adoption, and account health.\n This is a highly visible leadership role with the opportunity to define what world-class customer experience looks like for a developer-first AI platform and infrastructure company.\n What You’ll Do \n \n Define and own Lightning AI’s customer experience strategy across onboarding, implementation, adoption, technical success, support coordination, retention, and expansion.\n Build, lead, and develop a high-performing global team of Technical Account Managers and Support Engineers.\n Establish account-health frameworks that combine product usage, platform reliability, workload performance, customer sentiment, support activity, and commercial risk.\n Create clear escalation and incident-management processes that provide customers with fast response times, strong ownership, seamless internal coordination, and proactive communication.\n Define and track the metrics that matter, including time to value, activation, platform adoption, workload growth, retention, expansion, customer health, resolution time, and customer satisfaction.\n Turn customer conversations and usage patterns into actionable insights for product and engineering.\n Scale the function thoughtfully, balancing high-touch customer engagement with automation, self-service resources, and repeatable programs.\n \n What You’ll Need \n \n 10+ years of experience across customer experience, customer success, technical account management, solutions architecture, professional services, or a related customer-facing function.\n Growth-stage, enterprise SaaS, developer platform, cloud infrastructure, or AI infrastructure environment preferred, but not required\n A proven trac","salary_min":155000,"salary_max":220000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["gpu","mlops","distributed-systems","cloud","pytorch"],"apply_url":"https://job-boards.greenhouse.io/lightningai/jobs/7821031003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T03:29:03Z","expires_at":"2026-08-30T14:03:51.452084Z","created_at":"2026-07-30T14:03:45.87698Z","updated_at":"2026-07-31T14:03:51.583879Z","company_name":"Lightning AI","company_slug":"lightning-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=lightning.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c2f24669-9a25-4737-a0de-fe1453d33f0d"},{"id":"695debc1-7b4a-40f3-8ba9-0d3393471571","company_id":"b459414f-fd43-42c4-a6e1-f07225286a75","title":"Research Infrastructure - Member of Technical Staff","slug":"member-of-technical-staff-2cec38c1","description":"ABOUT THE COMPANY\n\nSimile is The Simulation Company. We simulate human behavior to keep people at the center of the decisions that shape the world. With AI, anyone can create a product, a campaign, a policy, or a script — the bottleneck has moved upstream. The hard question is no longer whether you can create something, but what to create, for whom, and how to bring it to life. Those are fundamentally human decisions, and they shouldn't be left to chance or handed off to an algorithm. We're building the infrastructure to understand human behavior at scale and to represent humans in an increasingly agentic world. Our mission is to simulate all eight billion people on earth.\n\n\n\nWe launched five months ago. Since then we've grown revenue 5x, built a new foundation model for human behavior that has run tens of millions of simulations for F100 enterprises, trained a first-of-its-kind confidence model that predicts the accuracy of every simulation, and released the first product that lets organizations verifiably predict the future. The world's leading companies use Simile to make business-critical decisions — from consumer leaders like CVS Health and Wealthfront to professional services organizations like Deloitte and Gallup — strategizing product launches, entering new markets, and forecasting earnings calls.\n\n\n\nWe've raised over $200M at a $2B post-money valuation led by Greenoaks, with Index Ventures, Hanabi, A*, Bain Capital Ventures, and CVS Health Ventures. We've grown from a small home in Palo Alto to a global team of 50+, and we're building a team of the best researchers, engineers, designers, and operators in the world. The future is too important to be left to chance.\n\n\n\n\n\nABOUT THE TEAM\n\nResearch Infrastructure builds the systems that every step of the model lifecycle runs on: data ingestion and schema design, distributed training, evaluation, serving, and monitoring. We are the reason a researcher's hypothesis can become a production simulation in days rather than quarters.\n\n\n\nTwo things make this problem unusual. First, our research-to-product pipeline is unusually tight - the experimental methods we validate on Monday are integrated into systems customers use to make high-stakes decisions. Second, simulating a society means running inference over populations of agents, not single requests. A single customer study can mean millions of model calls with interdependent state. Cost per simulation and latency per agent are not back-office metrics for us; they determine what research is even possible to run.\n\n\n\n\nABOUT THE ROLE\n\nAs a Member of Technical Staff in Research Infrastructure, you will build the platform our researchers train, evaluate, and deploy on - and own it through the last mile, where a trained checkpoint becomes a production service serving millions of interdependent agent calls at a cost per simulation we can afford.\n\n\n\nThis is a role for someone who is energized by both halves of that. You will spend some weeks designing the data schemas and training pipelines a research team depends on, others profiling a serving path to find where the FLOPs and GPU memory are going, and others still bringing up cluster nodes or deleting the third redundant copy of a code path. The common thread is leverage: every improvement you make compounds across every researcher and every simulation we run.\n\n\n\nWe are looking for engineers who find it gratifying to see their work pushed to its absolute limits, and who own problems end-to-end - including the last mile of deployment that most people would rather hand off.\n\n\n\n\nIN THIS ROLE, YOU WILL\n\n - Build the ML platform our researchers live in. Design and operate the services, libraries, and tooling that cover the full lifecycle - data exploration, feature generation, experiment tracking, training orchestration, evaluation, and deployment. Success is defined by your ability to increase experiment velocity, streamlining the researcher’s path from ideation to a fully validated, production-ready model.\n\n - Make training and data pipelines fast. Own throughput end to end: model FLOPs utilization across our training configs, tokenization cost when the data mix changes, and ingestion paths that take hours today where they should take minutes. Profile where the time and GPU memory actually go, then fix it, including the observability that makes the next bottleneck obvious before it bites.\n\n - Make serving fast and cheap enough to run a society. Own the inference path our simulations run on: batching and scheduling, KV cache reuse across agents sharing context, quantization, and the request patterns unique to population-scale runs where one study is millions of interdependent calls. Cost per simulation and latency per agent decide what research we can afford to run at all, so treat them as research constraints, not ops metrics.\n\n - Scaling simulation Data. Lead the redesign of our data architecture to handle the complexity and sheer volume of our simulation mode","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["mlops","pytorch","gpu","generative-ai","distributed-systems","agents","search","data-pipeline"],"apply_url":"https://jobs.ashbyhq.com/simile/9acc73e4-7f77-49dc-b0f6-1a9d26b33b04/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T01:26:30.32Z","expires_at":"2026-08-30T14:11:51.806559Z","created_at":"2026-04-22T15:46:51.33414Z","updated_at":"2026-07-31T14:11:51.928823Z","company_name":"Simile","company_slug":"simile","company_logo_url":"https://www.google.com/s2/favicons?domain=simile.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/695debc1-7b4a-40f3-8ba9-0d3393471571"},{"id":"cc0d7d3c-5d48-448b-a332-3a75998f31e6","company_id":"9bd78d17-fe7f-46b8-82c9-7c3499c319cd","title":"Open Source AI Engineer (Typescript)","slug":"open-source-ai-engineer-typescript-db5c66c2","description":"About Arize\n AI is rapidly transforming the world. As generative AI reshapes industries, teams need powerful ways to monitor, troubleshoot, and optimize their AI systems. That’s where we come in. Arize AI is the leading AI \u0026 Agent Engineering observability and evaluation platform , empowering AI engineers to ship high-performing, reliable agents and applications. From first prototype to production scale, Arize AX unifies build, test, and run in a single workspace—so teams can ship faster with confidence.\n We’re a Series C  company backed by top-tier investors,   with  over $135M in funding  and a rapidly growing customer base of  150+ leading enterprises and Fortune 500 companies.  Customers like  Booking.com , Uber, Siemens, and PepsiCo leverage Arize to deliver AI that works.\n The Opportunity \n AI is rapidly transforming the world. Whether it’s developing the next generation of human-level intelligence, enhancing voice assistants, or enabling researchers to analyze genetic markers at scale, AI is increasingly integrated into various aspects of our daily lives.\n Arize AI is the leading AI observability and evaluation platform, empowering AI engineers to build and deploy high-performing, reliable models. As the AI landscape shifts from traditional ML to generative AI and agentic systems, Arize ensures teams have the tools to monitor, troubleshoot, and improve AI in production.\n We’re looking for an Open Source AI Engineer to join our growing OSS team to drive the development of new frameworks, metrics, and tooling that help people build, test, and improve LLM tasks. You’ll play a lead role in shaping how developers measure and understand performance in advanced AI systems, all in the open.\n What You’ll Work On \n \n Build LLM Observability Frameworks: Design, architect, and open-source new libraries, pipelines, and APIs that make it simpler to monitor, evaluate, and improve LLM output.\n Collaborate with the Community: Partner closely with the broader AI open source ecosystem, gather feedback, review pull requests, and steer the direction of the project to address real developer needs.\n Prototype and Iterate Rapidly: Experiment with state-of-the-art LLM techniques, turning research into practical developer tooling.\n Improve Observability and Debugging: Integrate with our existing platform to surface deeper insights on LLM behavior—help teams quickly diagnose and fix issues such as hallucinations or bias.\n Educate and Evangelize: Write blog posts, white papers, tutorials, and documentation to help developers succeed with our open source tools and grow the LLM eval community.\n \n What We’re Looking For \n We’re looking for an engineer who’s deeply passionate about AI, loves working in the open, and thrives in a fast-paced environment where “everyone wears multiple hats”. You likely share our core values:\n \n Open Source Champion: You believe collaboration and community-driven development unlocks the best innovations.\n Creative Problem Solver: You enjoy tackling ambiguous challenges and finding elegant technical solutions.\n Data \u0026 Metrics Driven: You value empirical results, enjoy creating or refining evaluation metrics, and iterate based on real-world feedback.\n Technically Curious: You’re always learning—exploring new LLM architectures, prompt engineering strategies, or emerging library standards.\n Builder Mindset: You relish the process of taking ideas from initial prototypes to production-ready solutions that delight users.\n \n Desired Skills \u0026 Experience \n \n Hands-on LLM Experience: Familiarity with popular LLM frameworks, prompt engineering techniques, and agent harnesses.\n Strong TypeScript Proficiency: You understand TypeScript and can write isomorphic code that executes seamlessly across clients, servers, and diverse edge runtimes.\n Open Source Track Record: Contributions to open source projects, personal GitHub repos with interesting AI demos, or a history of active engagement in developer communities.\n ML Observability \u0026 Tools: Familiarity with debugging AI applications, exploring embeddings, or building data-heavy dashboards is a plus.\n \n Why Work With Us \n \n Shape the Future of AI Evaluation: Be at the forefront of designing new ways to measure and improve next-generation LLMs.\n High Impact, Real Ownership: Join a team that values autonomy and speed. You’ll drive major initiatives from day one and see your work used by developers worldwide.\n Fully Remote, Flexible Environment: We are a fully remote company with offices in the Bay Area and NYC for those who prefer in-person collaboration.\n Cutting-Edge Challenges: Our platform already helps analyze millions of AI predictions daily, giving you the chance to refine your evaluation tooling on real, large-scale production workloads.\n Work With a Talented, Passionate Team: Collaborate closely with top engineers who are dedicated to making AI more transparent, reliable, and impactful.\n \n The estimated annual salary and var","salary_min":185000,"salary_max":200000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"mid","tags":["agents","llm","generative-ai","typescript"],"apply_url":"https://job-boards.greenhouse.io/arizeai/jobs/6119757004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-29T22:10:40Z","expires_at":"2026-08-30T14:03:58.968363Z","created_at":"2026-07-30T14:03:53.474251Z","updated_at":"2026-07-31T14:03:59.108226Z","company_name":"Arize AI","company_slug":"arize-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=arize.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/cc0d7d3c-5d48-448b-a332-3a75998f31e6"},{"id":"d4ea1ff8-70df-49cc-9765-24075e42ced0","company_id":"e5e49ca2-fa01-4747-a951-326be14de524","title":"Staff ML Engineer, Agent Training \u0026 Environments","slug":"staff-ml-engineer-agent-training-environments-c9d2eab1","description":"Shape the Future of AI \n At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.\n About Labelbox \n We're the only company offering three integrated solutions for frontier AI development:\n \n Enterprise Platform \u0026 Tools : Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale\n Frontier Data Labeling Service : Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models\n Expert Marketplace : Connecting AI teams with highly skilled annotators and domain experts for flexible scaling\n \n Why Join Us \n \n High-Impact Environment : We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions.\n Technical Excellence : Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.\n Innovation at Speed : We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.\n Continuous Growth : Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.\n Clear Ownership : You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.\n \n Role Overview\n Labelbox is the RL data factory for advancing frontier agent capabilities. We build the data, environments, and evaluations that frontier labs use to train and judge their agents.\n This role sits where training meets infrastructure. You will run the experiments and build the systems that run them: environments agents act in, verifiers that decide whether they succeeded, and the fine-tuning pipelines that turn that signal into a better model. We're looking for someone who does both halves — the engineering throughput of a strong platform engineer, and real depth in post-training agents.\n The bar is high: engineers with strong judgment who set technical direction, turn prototypes into reliable systems fast, and are at the frontier of agent-first engineering practice.\n  \n What you'll work on\n \n RL environments for agentic tasks: task definitions, tool surfaces, state and reset semantics, reward design — and the harness that runs thousands of them in parallel.\n Verifiers and graders: programmatic checks, LLM judges, rubric pipelines, pass@k scoring. Deciding what \"the agent succeeded\" means, and making that judgment trustworthy at scale.\n Fine-tuning pipelines that turn evaluation signals into measurable agent improvements — SFT and RL, from data collection through training to checkpoint evaluation.\n Eval systems that run millions of agent trajectories to measure model and product quality.\n Training and serving infrastructure that scales to the throughput frontier labs need: multi-launcher orchestration, long-running job fault tolerance, cost accounting.\n \n What we're looking for\n As an engineer \n \n A 3+ year track record of shipping systems that customers and other engineers still rely on.\n Exceptional throughput, without the quality tax. You ship a lot, you review a lot, and the v1 you ship becomes the foundation the rest of the team builds on.\n Strong system and API design judgment. Hard architecture calls land with you: you make them, defend them under pressure, and update fast when someone else is right.\n You ship production code with coding agents daily. You know where they break and what it takes to make them reliable, and you use that to move the whole team faster.\n You build the substrate other people's work runs on — tooling, CI, harnesses, libraries — and you treat that as the job, not a distraction from it.\n You move fast in ambiguous, startup-pace environments, with influence over authority.\n Deep proficiency in Python, and comfort across the rest of the stack.\n \n As an RL post-training practitioner \n \n You have fine-tuned models for agentic tasks and made them measurably better. SFT plus at least one RL method (GRPO, PPO, DPO, or similar) in production.\n You have built environments agents operate in, and you know why reward and task design is where most of the difficulty actually lives.\n You have designed verifiers or graders for open-ended work, and you know how they get gamed.\n You debug training runs forensically and methodically.\n You reason about compute-economics. You know what an experiment costs, when a run is not worth finishing, and how to get the same signal for a tenth of the spend.\n You write up what you learned so it changes what the team does next.\n \n","salary_min":250000,"salary_max":280000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","agents","api-design","llm","distributed-systems","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/labelbox/jobs/5199053007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-29T21:20:44Z","expires_at":"2026-08-30T14:05:20.057069Z","created_at":"2026-07-30T14:05:19.341348Z","updated_at":"2026-07-31T14:05:20.191336Z","company_name":"Labelbox","company_slug":"labelbox","company_logo_url":"https://www.google.com/s2/favicons?domain=labelbox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d4ea1ff8-70df-49cc-9765-24075e42ced0"},{"id":"52277b68-b383-40c7-8131-7a3ae5014e26","company_id":"f0134765-5cdf-4b32-b956-b7a147d9d415","title":"Senior Manager, Data Engineering","slug":"senior-manager-data-engineering-c76ddd18","description":"Role Description\n \n We are seeking a Senior Manager, Data Engineering to lead the team responsible for the reliability, quality, cost, and velocity of Dropbox's core data platform. This is a hands-on engineering leader who owns the pipelines and data products that Product, GTM, Finance, and the CTO organization depend on to make decisions. \n  \n In this role, you will lead and grow a team of data engineers building and operating our ingestion, transformation, orchestration, and serving layers, as well as the self-serve analytics substrate that lets partner teams answer their own questions without bespoke engineering work. \n  \n The ideal candidate is a deeply technical, product-minded engineering leader who can hold a high bar on system reliability and data quality while partnering closely with Data Science, Business Intelligence Engineering, Analytics, and Product to turn fragmented, ticket-driven data work into durable, reusable data products. \n Responsibilities\n \n \n Data Quality \u0026 Observability: Establish and enforce a rigorous data quality culture: lineage, freshness monitoring, anomaly detection, and outcome-oriented, gaming-resistant quality metrics. \n \n Self-Serve Platform: Lead the engineering of the self-serve analytics substrate, reducing bespoke request volume and increasing partner-team autonomy. \n \n Cost \u0026 Efficiency: Own the unit economics of the data platform — compute and storage efficiency — and drive measurable improvements without sacrificing reliability. \n \n Cross-Functional Partnership: Partner deeply with Data Science, BIE, Analytics, Product, Data Platform, and the CTO org to define the semantic layer, modeling standards, and data contracts that make downstream work trustworthy and fast. \n \n Engineering Culture: Establish rigorous engineering practices — code review, testing, CI/CD for data, incident response, and postmortems — and champion the effective, measured use of AI coding tools to improve engineering productivity. \n \n Team Leadership: Lead, mentor, and grow a high-talent-density team of data engineers, fostering a culture of ownership, technical excellence, psychological safety, and continuous learning. \n \n Requirements\n \n \n 8+ years of data engineering or backend/data infrastructure experience with increasing scope, ideally in high-scale environments. \n \n 3+ years of experience directly managing and growing engineering teams, including hiring, coaching, performance management, and team design. \n \n Deep Technical Expertise: Proven track record building and operating large-scale batch and streaming pipelines (e.g., Spark, dbt, Airflow/orchestration) on a modern lakehouse or warehouse stack (e.g., Databricks, Snowflake, BigQuery). \n \n Reliability \u0026 Quality: Demonstrated ownership of data SLAs, observability, lineage, and incident response for business-critical pipelines. \n \n Systems \u0026 Modeling: Strong data modeling fundamentals and the ability to design a semantic layer and data contracts that serve many downstream consumers. \n \n Stakeholder Management: Excellent communication and the ability to align engineering, data science, analytics, and business partners around shared reliability and quality goals. \n \n Preferred Qualifications\n \n \n Platform / Self-Serve Experience: Track record building self-serve data or analytics platforms that reduced bespoke request volume and increased partner autonomy. \n \n AI-Forward Engineering: Experience integrating AI coding tools and LLM-based tooling into the engineering workflow, with a measured approach to impact and guardrails. \n \n Cost Discipline: Demonstrated success improving compute/storage unit economics without regressing reliability. \n \n Familiarity with modern data governance, privacy, and access-control practices. \n \n Experience operating in a pod or embedded model serving multiple business partners. \n \n \n Durable Skills \n AI fluency means using these tools to amplify human judgment, not replace it. We believe people with these skills will thrive as work and technology continue to evolve: \n \n Awareness: U nderstand yourself and others . \n Judgment: E valuat e information and mak e decisions in complex situations . \n Adaptability: L earn, adjust, and stay effective through change . \n Connection: C ommunicat e , collaborat e , and build trust . \n \n To learn more about why these skills matter and what the data shows about thriving through change, read this blog post from our Chief People Officer, Melanie Rosenwasser. \n Compensation \n US Zone 1 \n This role is not available in Zone 1 \n US Zone 2\n $202,700 — $274,300 USD \n US Zone 3\n $180,200 — $243,800 USD","salary_min":180200,"salary_max":243800,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["llm","mlops","data-engineering","data-science"],"apply_url":"https://jobs.dropbox.com/listing/8090062?gh_jid=8090062","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-29T21:04:04Z","expires_at":"2026-08-30T14:09:49.140781Z","created_at":"2026-07-30T14:09:22.265716Z","updated_at":"2026-07-31T14:09:49.270687Z","company_name":"Dropbox","company_slug":"dropbox","company_logo_url":"https://www.google.com/s2/favicons?domain=www.dropbox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/52277b68-b383-40c7-8131-7a3ae5014e26"},{"id":"26c78124-fdbf-475f-8684-21a68bdecb67","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Chief Engineer, Autonomous Flight","slug":"chief-engineer-autonomous-flight-0bcdc454","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n About the Job   \n The Air Dominance \u0026 Strike team at Anduril develops aerial and multi-domain robotic systems. The team is responsible for taking products like Fury (unmanned fighter jet) and Barracuda (air-breathing cruise missile) from concept to product. The team also develops Lattice for Mission Autonomy, Anduril’s premier software platform that enables masses of Fury, Barracuda, and other first and third-party robots to collaborate across various missions. We work in close coordination with specialist teams like Perception, Motion Planning, Hardware, and Test Engineering to solve some of the hardest problems facing our customers.   \n As a Chief Engineer, you will own the technical architecture and system-level design of the onboard autonomy stack, command and control systems, and modular payloads for our next-generation autonomous platforms. With a software heavy focus, you will guide lean, multi-disciplinary engineering teams from \"0-to-1\" design through deployment, bridging the gap between high-level business strategy and rigorous engineering execution.   \n What You'll Do:   \n \n Lead systems engineering, requirements decomposition, and technical architecture for software-heavy robotics systems.   \n \n \n Guide small, fast-moving groups of individual contributors through the design, implementation, and deployment phases of emerging programs.    \n \n \n Oversee hardware/software integration and testing (simulation, hardware-in-the-loop, and field testing) to ensure perception, motion planning, and task allocation algorithms run seamlessly on physical platforms.    \n \n \n Partner with Product Managers and Business Development to convert customer mission needs and operational concepts into concrete technical milestones.    \n \n \n Decide technical trade-offs across concurrent programs, helping teams prioritize engineering efforts.   \n \n Required Qualifications:   \n \n Proven track record directing technical architecture and guiding individual contributors as a lead engineer   \n \n \n Background in developing and fielding software-heavy robotics, autonomous systems, or aerospace products.   \n \n \n Strong hardware/software integration skills, including robotics software architectures and tactical mission systems.   \n \n \n Experience with autonomous systems, mission-critical Department of Defense systems, or tactical edge technologies.   \n \n \n Travel:  Ability to travel up to 25% to customer sites and field testing locations.   \n \n \n Education:  BS, MS, or PhD in Robotics, Aerospace, ME, EE, CS, or equivalent.   \n \n \n Security Clearance:  Eligible to obtain and maintain an active U.S. Secret clearan   \n \n Preferred Qualifications:   \n \n Advanced knowledge in Motion Planning, Perception, Command \u0026 Control, SLAM, task allocation, or tactical networking.   \n \n \n Work history involving multi-domain unmanned systems (air, ground, or maritime).   \n \n \n Understanding of edge-AI/ML deployment on autonomous physical systems.   \n \n   \n US Salary Range\n $220,000 — $330,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:   \n  \n Benefits \n At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next.  For more information, Explore Our Benefits . \n  \n \n Protecting Yourself from Recruitment Scams \n Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal informa","salary_min":220000,"salary_max":330000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["payments","cloud","robotics","computer-vision","gpu"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5158222007?gh_jid=5158222007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-29T19:46:59Z","expires_at":"2026-08-30T14:07:46.508717Z","created_at":"2026-07-30T14:07:20.585832Z","updated_at":"2026-07-31T14:07:46.679205Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/26c78124-fdbf-475f-8684-21a68bdecb67"},{"id":"53b083b9-1530-4dcb-b335-4fb71a87a4ec","company_id":"af3a34e9-b9f3-4d69-bcf2-f13327711b7d","title":"Staff Product Manager, Applied AI","slug":"staff-product-manager-applied-ai-174941ef","description":"Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.\n JOB SUMMARY\n You will focus on driving the product vision, ensuring seamless execution across the complex hardware/software divide, and translating market demands into user oriented, executable product roadmaps.\n ESSENTIAL DUTIES AND RESPONSIBILITIES / KEY ACCOUNTABILITIES\n \n Own and maintain the physical AI model roadmap: product vision, milestones, and release cycles for the core model stack\n Drive the integration, adaptation, and fine-tuning of foundation models into Apollo's autonomy stack. Sequence which capabilities graduate into the shipping model, balancing capability velocity against reliability and safety.\n Translate customer use cases into model capability requirements and acceptance criteria\n Partner with our data collection and evaluation team on benchmarks, real-world evaluation, and the success criteria that gate real-world deployment.\n Own the data strategy that feeds model development: define what to collect and why, the target tasks and behaviors, and the quality bar.\n Align data technology and operations teams on collection techniques, tooling, and quality expectations.\n Define and prioritize use cases based on business impact, feasibility, and customer needs.\n Ensure that product development efforts align with real-world economic value for customers.\n Synthesize insights from user research, customer meetings, usage data, and sales feedback into a strategy that delivers business objectives and customer benefits.\n Work with industrial design, user experience, and engineering teams to conceptualize and implement interactive features.\n Define product features/enhancements and communicate requirements to engineering teams via clearly written requirements documents, diagrams, and concise verbal communication.\n Support product marketing initiatives, partner relationships, and other opportunities to accelerate the adoption of our products.\n \n SKILLS AND REQUIREMENTS\n \n 7+ years of experience in product management\n Strong understanding of modern robot learning techniques including imitation learning, reinforcement learning\n Demonstrated experience integrating, adapting, or fine-tuning models\n Strong familiarity with the constraints of running large neural networks on physical edge devices\n Strong analytical skills with experience in performance tracking and data-driven decision-making.\n Excellent communication skills, both verbal and written, enabling translation of technical insights into business impact.\n You make decisions in uncertainty, prioritizing velocity over perfection\n You prioritize user feedback, promoting and advocating for that voice to internal teams.\n \n NICE TO HAVE\n \n Experience with autonomous robots or autonomous vehicles.\n Experience using simulation tools to improve user experience.\n Hands-on experience training or fine-tuning foundation\n Experience with data collection operation (teleoperation fleets, annotation and curation pipelines).\n Experience working with customers in light and heavy duty industrial environments\n Experience with robotics and functional safety standards (ISO 10218, ISO/TS 15066, ISO 13482, RIA 15.08).\n \n EDUCATION and/or EXPERIENCE\n \n Bachelor's degree (or equivalent) in mechanical, electrical, systems, or robotics engineering, or a related field; advanced degree or MBA helpful.\n At least 10 years of experience in product management or engineering, including 5+ years leading product managers, with a track record of shipping complex hardware products.\n \n PHYSICAL REQUIREMENTS\n \n Prolonged periods of sitting at a desk and working on a computer.\n Ability to spend time on lab and manufacturing floors, and to travel to supplier and manufacturing sites as needed.\n Must be able to lift 15 pounds at times.\n Vision to read printed materials and a computer screen; hearing and speech to communicate. The annual salary range is $215,000 - $245,000\n  \n  \n *This is a direct hire.  Please, no outside Agency solicitations. \n Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, ","salary_min":215000,"salary_max":245000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["deep-learning","autonomous-vehicles","reinforcement-learning","fine-tuning","generative-ai","healthcare","robotics"],"apply_url":"https://boards.greenhouse.io/apptronik/jobs/6120107004?gh_jid=6120107004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-29T19:41:00Z","expires_at":"2026-08-30T14:13:38.325787Z","created_at":"2026-07-30T14:13:24.845031Z","updated_at":"2026-07-31T14:13:38.459052Z","company_name":"Apptronik","company_slug":"apptronik","company_logo_url":"https://www.google.com/s2/favicons?domain=apptronik.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/53b083b9-1530-4dcb-b335-4fb71a87a4ec"},{"id":"26ff78f6-343e-4984-9a0b-7cc44695d2ad","company_id":"19955a21-2cd6-41fd-a4a8-19b7a942ac16","title":"Senior Value Engineer - Public Sector","slug":"senior-value-engineer-public-sector-03f25565","description":"Celonis is the global leader in Process Intelligence and the pioneer of Process Mining technology. As one of the world’s fastest-growing enterprise SaaS companies, we are changemakers pushing the boundaries of what’s possible. We invest heavily in advanced AI capabilities—specifically our Process Intelligence Graph—to turn data insights into immediate business action. We believe there is a massive opportunity to unlock global productivity and sustainability by placing intelligence at the core of every business process. Join our mission to make processes work for people, companies, and the planet.\n Role Description \n As a Senior Value Engineer specializing in the Public Sector, you are pushing the envelope in solving critical operational challenges for State and Local Government agencies. You will be working with our most strategic public sector clients, understanding their unique objectives—from enhancing citizen services and modernizing legacy systems to optimizing grant management and regulatory compliance—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners, such as Microsoft, OpenAI, and Databricks. With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the complex realities of government operations (breaking down agency silos) and enables agencies to industrialize AI. This unlocks real ROI on AI deployments, maximizing taxpayer value at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to government CIOs and Agency Directors, and ensure successful implementation, adoption, and value realization to increase the footprint of Celonis across state and local governments.\n Key Responsibilities: \n \n \n AI Discovery \u0026 Solutioning: Understand public sector AI strategies and specific agency challenges (e.g., benefits administration backlogs, procurement bottlenecks, public safety resource allocation, or permitting delays). As a Celonis product and government domain expert, find the best problem-solution fit and translate agency requirements into innovative solutions that deliver measurable public impact.\n \n Pre- and Post-Sales Execution: Actively drive the full customer lifecycle within the public sector. Lead technical discovery and capability demonstrations during the complex government pre-sales and procurement (RFP) cycles, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully met.\n \n Hackathons \u0026 Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex legacy processes. Leverage cutting-edge AI technologies to rapidly build creative prototypes in agency hackathons, solving critical pain points to improve constituent experiences.\n \n Agentic Process Transformation: Support our government customers in achieving real value out of AI deployments at scale, enabling a fundamental shift from traditional, rigid, paper-heavy workflows to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent case triaging or automated compliance checks).\n \n Proof Projects: End-to-end execution of critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with government enterprise data, identity protocols, and stringent compliance/security frameworks (e.g., StateRAMP, HIPAA, CJIS).\n \n Domain \u0026 Industry Leadership: Serve as the internal and external technical subject matter expert for the State \u0026 Local Government vertical, scaling knowledge across the organization regarding agency processes and public sector nuances.\n \n Requirements: \n \n \n 5+ years of experience leading technical pre-sales and post-sales engagements specifically within the Public Sector (State \u0026 Local Government). This includes navigating government procurement cycles, building compelling ROI business cases for public funds, and guiding technical implementations through to constituent value realization.\n \n Deep understanding of business processes native to state and local governments (such as Health \u0026 Human Services, Procurement, Finance, DMV operations, or Public Safety) with the ability to translate high-level policy or agency needs into specific, impactful AI use cases.\n \n Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent constituent-facing chatbots, automated processing of policy documents, or grant application analysis).\n \n Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.\n \n Strong presentation skills to both internal and external ","salary_min":156000,"salary_max":183000,"location":"Redwood City, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","agents","pytorch","generative-ai","cloud","llm"],"apply_url":"https://job-boards.greenhouse.io/celonis/jobs/7817311003?gh_jid=7817311003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-29T19:37:33Z","expires_at":"2026-08-30T14:09:25.09002Z","created_at":"2026-07-30T14:08:58.534188Z","updated_at":"2026-07-31T14:09:25.214287Z","company_name":"Celonis","company_slug":"celonis","company_logo_url":"https://www.google.com/s2/favicons?domain=www.celonis.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/26ff78f6-343e-4984-9a0b-7cc44695d2ad"},{"id":"862e6737-af2a-43b2-8d28-3f1aca3b1716","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Machine Learning Manager, Feed Relevance (Retrieval)","slug":"machine-learning-manager-feed-relevance-retrieval-a91727ea","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Reddit is looking for an experienced Engineering Manager to lead our Feed Retrieval team. In this role, you’ll lead a high-impact team of Machine Learning Engineers building the systems that identify, retrieve, and shape the candidate inventory powering Reddit’s personalized feeds. Your team will work at the foundation of Feed Relevance: expanding the set of high-quality content Reddit can recommend, improving personalization and discovery for users across different levels of signal, and building scalable ML systems that directly shape the experiences of over 120M+ daily users. If applying ML / AI in production to improve Reddit Relevance excites you, then you’ve found the right place.\n Responsibilities: \n \n Define Technical Vision \u0026 Strategy: Define the technical vision and long-term roadmap for Feed Retrieval, aligning large-scale recommender-system investments with Reddit’s product, ecosystem, and business objectives.\n Roadmap \u0026 Prioritization: Translate broad Feed Relevance goals into a focused team roadmap, making clear prioritization tradeoffs across model quality, inventory expansion, experimentation velocity, infrastructure cost, and operational reliability.\n Team Leadership \u0026 Development: Coach and support the development of your team, constantly seeking opportunities to grow their skills and impact. \n Technical Execution \u0026 Delivery: Oversee the design, development, and optimization of retrieval systems that source relevant, diverse, fresh, and high-quality candidates for personalized feed experiences.\n Measurement \u0026 Learning: Establish strong measurement, experimentation, and debugging practices so the team can understand retrieval quality, candidate coverage, source incrementality, and downstream impact.\n Platform \u0026 Infrastructure Collaboration: Collaborate with ML platform, infrastructure, ranking, safety, and product teams to build scalable, low-latency retrieval systems that can support the next generation of AI-powered recommendations.\n Operational Excellence: Maintain high standards for system performance, reliability, latency, cost efficiency, and responsible recommendation practices.\n Cross-Functional Partnership: Work with cross-functional partners from across the company to identify key areas of opportunity, set expectations, and communicate your team’s work.\n Recruiting \u0026 Growth: Partner with our incredible recruiting team to attract, interview, and hire diverse and talented machine learning engineers, growing a world-class team.\n \n Qualifications: \n \n Experience Leading ML Teams: 2+ years of experience building and managing high-performing ML or recommender-systems teams.\n Deep ML Expertise: Hands-on experience with large-scale production ML systems, ideally including recommender systems, retrieval models, embedding-based systems, sequence models, transformer-based architectures, or LLM-powered recommendation applications.\n Technical Domain Knowledge: Strong understanding of recommender systems, especially candidate retrieval, embedding/indexing systems, ranking handoffs, feed personalization, exploration, content quality, and measurement strategies. \n Strategic Thinking: Ability to develop and communicate a clear technical strategy across ambiguous problem spaces, balancing user relevance, ecosystem health, system scalability, and business impact.\n Impact-Driven Mindset: Passion for developing scalable, well-designed, and responsible AI solutions that drive business value.\n Exceptional Communication \u0026 Collaboration: Strong interpersonal skills and a collaborative mindset, with the ability to effectively communicate complex technical topics to diverse audiences and build strong relationships with cross-functional partners.\n \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\n Family Planning Support\n Gender-Affirming Care\n Mental Health \u0026 Coaching Benefits\n Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave \n \n #LI-remote, #LI-JS5\n Pay Transparency: \n This job posting may span more than one career level.\n In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) prog","salary_min":253300,"salary_max":354600,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"junior","tags":["healthcare","llm","fine-tuning","evaluation","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8094985","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-29T19:24:35Z","expires_at":"2026-08-30T14:09:31.295056Z","created_at":"2026-07-30T14:09:04.880987Z","updated_at":"2026-07-31T14:09:31.418717Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/862e6737-af2a-43b2-8d28-3f1aca3b1716"},{"id":"62bc42b9-5266-4541-8c1e-b4a390d629a1","company_id":"47c8818e-9a45-4180-8d96-931d2774d36b","title":"Senior Applied ML Engineer","slug":"senior-applied-ml-engineer-39a82ca2","description":"About Upstart \n At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.\n As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 1,800 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.\n We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City (opening Summer 2026), you’ll have the support to work in the way that works best for you.\n If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you.\n The Team \n Upstart’s Applied LLM team is building foundational infrastructure that democratizes access to generative AI for every product and engineering team across the company.  This is a cross-functional team at the intersection of machine learning, product, and engineering. Our mission is to bring the power of ML, particularly large language models (LLMs) and generative AI, to life in Upstart’s core products.\n As a Senior Applied Machine Learning Engineer focused on building Upstart's LLM applications, you'll work closely with researchers, product managers, platform engineers, and designers to ship intelligent features that elevate the user experience and expand the capabilities of our systems.\n How you’ll make an impact: \n \n Design and build user-facing ML features that harness LLMs and generative AI to unlock new product capabilities\n Partner with product, design, and ML research to prototype and deliver high-impact, ML-powered experiences\n Own the technical architecture and implementation strategy for applied ML systems - balancing latency, observability, and iteration speed\n Build scalable services and APIs that bring model outputs to users in trustworthy and intuitive ways\n Collaborate across platform, infra, and legal/compliance teams to ensure ML deployments meet standards for safety, fairness, and performance\n Establish and evangelize best practices for prompt design, model evaluation, and experimentation across the org\n \n What we’re looking for:  \n \n Minimum qualifications: \n \n 4+ years of software engineering experience, with 2+ years working directly on ML-driven products or intelligent systems\n Proven ability to lead complex initiatives across engineering, product, and research stakeholders\n Strong backend development skills (e.g., Python with FastAPI or Flask), plus experience with cloud-native tooling (e.g., Kubernetes, Docker, Terraform)\n Experience integrating LLMs or ML models into production systems, including APIs and user-facing applications\n Excellent communication skills and a collaborative, product-minded approach\n Ability to think rigorously about system design, latency tradeoffs, and user impact when working with ML features \n \n Preferred qualifications: \n \n Experience shipping GenAI or LLM-powered features using frameworks like LangChain, LlamaIndex, or OpenAI APIs\n Familiarity with retrieval-augmented generation (RAG), vector search (e.g., FAISS, Pinecone), and real-time inference patterns\n Proficiency in full-stack development, including front-end work with React or similar frameworks\n Strong intuition for prompt engineering, model testing, and evaluation methodologies\n Experience navigating complex requirements around explainability, user trust, or compliance in ML applications\n Track record of influencing architecture or product direction at a team or org level\n \n \n Position location This role is available in the following locations: Remote\n Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.\n  \n #LI-REMOTE \n #LI-Associate \n #LI-","salary_min":177700,"salary_max":220000,"location":"United States","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["embeddings","generative-ai","rag","llm","agents","machine-learning"],"apply_url":"https://careers.upstart.com/jobs?gh_jid=8094141","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-29T19:08:51Z","expires_at":"2026-08-30T14:16:14.165909Z","created_at":"2026-07-30T14:16:08.133536Z","updated_at":"2026-07-31T14:16:14.264711Z","company_name":"Upstart","company_slug":"upstart","company_logo_url":"https://www.google.com/s2/favicons?domain=upstart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/62bc42b9-5266-4541-8c1e-b4a390d629a1"}],"page":1,"per_page":20,"total":8996,"total_is_exact":true,"total_pages":450}