{"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":"2b1514f0-20bf-4830-9fdc-23e5c0f8bbed","company_id":"a25140f5-772d-4c47-947d-7c655ed80737","title":"Senior Software Engineer - Normalization Platform","slug":"senior-software-engineer-normalization-platform-451aa2a7","description":"Senior Software Engineer - Normalization Platform \n Truveta is the world’s first health provider led data platform with a vision of Saving Lives with Data. Our mission is to enable researchers to find cures faster, empower every clinician to be an expert, and help families make the most informed decisions about their care. Achieving Truveta’ s ambitious vision requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our company values . \n This position is based out of our headquarters in the Greater Seattle area.  #LI-hybrid \n Who We Need   \n Truveta is rapidly building a talented and diverse team to tackle complex health and technical challenges. We are seeking candidates inspired by the opportunity to apply data in the development of real-world health solutions. Beyond core capabilities, we are seeking problem solvers, passionate and collaborative teammates, and those willing to roll up their sleeves while making a difference.     \n If you are interested in the opportunity to pursue purposeful work, join a mission-driven team, and build a rewarding career while having fun, Truveta may be the perfect fit for you.  \n This Opportunity   \n Truveta's mission is Saving Lives with Data: helping researchers find cures faster, empowering every clinician to be an expert, and helping families make the most informed decisions about their care. Semantic normalization is where that data becomes usable. Health systems describe the same diagnosis, lab result, or medication in countless ways, and our platform maps each one to a standard medical concept, so researchers can study data from many health systems as one. Every dataset Truveta delivers runs through it.   \n As a Senior Software Engineer, you will own major pieces of the platform's next-generation architecture. You will scale the service for both the volume and the variety of its workloads, building a flexible system that keeps pace with Truveta's growing data volume and expanding range of data sources.   \n We are seeking backend engineers who: \n \n \n Have proven experience building reliable, secure, and scalable software solutions using modern cloud-based paradigms   \n Are quick learners who can iterate on and evolve distributed software solutions while carefully balancing privacy, security, and reliability requirements with a changing product landscape   \n Have knowledge of and experience designing and working with distributed systems, data pipelines, and APIs   \n Can guide workstreams, mentor and inspire junior developers, and cultivate a positive work environment   \n Adhere to Truveta principles in support of ethical innovation   \n Are committed to ongoing training on key topics such as privacy, ethics, and security   \n \n Key Qualifications:   \n \n 5+ years of experience building production-quality software in a team setting   \n Experience building cloud services or data pipelines in Java, Python, or C#   \n Experience building and supporting cross-platform, multi-tenant services   \n 2+ years of experience on cloud-native platforms (AWS, GCP, or Azure)   \n B.S. or M.S. in Computer Science or a related field   \n InfraOps or DevOps experience is a plus   \n Kubernetes experience is a plus   \n Healthcare domain experience is a plus   \n Experience with AI agents is a plus   \n \n Why Truveta? \n Be a part of building something special. Now is the perfect time to join Truveta. We have strong, established leadership with decades of success. We are well-funded. We are building a culture that prioritizes people and their passions across personal, professional, and everything in between. Join us as we build an amazing company together. \n We Offer: \n \n Interesting and meaningful work for every career stage \n Great benefits package \n Comprehensive benefits with strong medical, dental and vision insurance plans \n 401K plan \n Professional development \u0026 training opportunities for continuous learning \n Work/life autonomy via flexible work hours and flexible paid time off \n Generous parental leave \n Regular team activities (virtual and in-person) \n The base pay for this position is $155,000 to $190,000. The pay range reflects the minimum and maximum target. Pay is based on several factors including location and may vary depending on job-related knowledge, skills, and experience. Certain roles are eligible for additional compensation such as incentive pay and stock options. \n \n If you are based in California, we encourage you to read this important information for California residents linked here. \n Truveta is committed to creating a diverse, inclusive, and empowering workplace. We believe that having employees, interns, and contractors with diverse backgrounds enables Truveta to better meet our mission and serve patients and health communities around the world. We recognize that opportunities in technology historically excluded and continue to disprop","salary_min":155000,"salary_max":190000,"location":"Seattle, WA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","healthcare","distributed-systems","data-pipeline","platform"],"apply_url":"https://job-boards.greenhouse.io/truveta/jobs/6223732004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T23:29:02Z","expires_at":"2026-11-13T17:00:07.879355Z","created_at":"2026-10-14T15:23:39.300558Z","updated_at":"2026-10-14T17:00:07.882558Z","company_name":"Truveta","company_slug":"truveta","company_logo_url":"https://www.google.com/s2/favicons?domain=truveta.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2b1514f0-20bf-4830-9fdc-23e5c0f8bbed"},{"id":"ebabc8b3-5891-442b-a4d0-7a3ac1388c2d","company_id":"aa372131-86ce-432a-af45-e2b42a79ba29","title":"Infrastructure Engineer, Agentic DevSecOps","slug":"infrastructure-engineer-agentic-devsecops-35467747","description":"ABOUT DISTYL AI \n\nDistyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.\n\nWe research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.\n\nDistyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. \n\n\n\n\nWHAT WE ARE LOOKING FOR\n\nWe're hiring a Infrastructure Engineer to own how Distyl engineers build, test, secure, and release software. Our independently versioned services span multiple repositories and deploy into customer environments across AWS, Azure, and GCP, with different security, connectivity, and upgrade constraints.\n\n\n\nThis is a hands-on role with scope matched to your experience, from owning complex delivery systems to setting technical direction across teams. You'll build shared delivery systems and coding-agent workflows that make releases reliable, failures actionable, and engineers more productive. Your systems will shape how every Distyl engineer ships.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Technical ownership. Set direction and priorities for build, test, release, and coding-agent infrastructure. Choose tools and standards with engineering partners, and own implementation, adoption, and operation across teams\n\n - Build systems and repository standards. Standardize repository conventions and reusable build/test workflows. Make builds reproducible and efficient through explicit dependencies, consistent tooling, and effective caching\n\n - Release engineering. Build once and promote versioned bundles of immutable artifacts through release stages, with source traceability and test and security gates. Partner with Platform, Runtime, and Customer Delivery on integration and compatibility testing, configuration and schema changes, and recovery. Support customer-registry distribution and offline packaging where required\n\n - Agent-enabled engineering. Equip engineers with reusable coding-agent tools to diagnose failures, propose fixes, and handle routine work. Include tests, scoped permissions, failure handling, and independent output validation; measure quality and time saved\n\n - Software supply-chain security. Implement artifact signing and provenance, software bills of materials (SBOMs), isolated build runners, scoped CI credentials, and vulnerability gates. Integrate dependency and container remediation into engineering workflows\n\n - Developer experience. Understand engineers' workflows and remove friction with self-service tools, documentation, and diagnostics that distinguish code failures from tooling issues. Use developer feedback to guide incremental migrations, earn adoption, and reduce manual work\n\n - Engineering productivity and alerting. Track build/test latency, pipeline reliability, flaky-test rates, release lead time, and tooling adoption. Use these signals and developer feedback to reduce flaky tests and bottlenecks. Set service-level objectives and route actionable alerts to owners\n   \n   \n\n\nWHO YOU ARE\n\n - 10+ years of experience in software engineering, developer productivity, SRE, or related infrastructure engineering roles\n\n - Experience enabling engineers to use coding agents through reusable tools, workflows, and guidance, with independent validation and scoped access\n\n - Experience owning production build, test, and release infrastructure across services and teams, including versioned release bundles and compatibility testing\n\n - Hands-on experience with build systems, dependency management, reproducibility, performance diagnosis, and caching\n\n - Strong Python skills for building and debugging backend services and maintainable automation. Comfortable navigating unfamiliar code, including JavaScript frontends\n\n - Strong Linux fundamentals and experience building and maintaining secure container images. Working knowledge of Kubernetes, GitOps, infrastructure as code, and cloud deployment\n\n - Hands-on experience implementing software supply-chain controls. Demonstrated security judgment across permissions, credentials, dependencies, artifacts, and remediation\n\n - Experience building developer platforms, automated remediation, or reusable agent tooling adopted by other engineers\n\n - A record of improving build/test performance, release reliability, and tooling adoption using metrics and developer feedback\n\n - Experience","salary_min":200000,"salary_max":250000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["microservices","agents","healthcare","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/distyl/6b13df85-e705-4a69-9340-0e83c54e78c5/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T23:19:53.802Z","expires_at":"2026-11-13T17:03:04.859555Z","created_at":"2026-10-14T15:25:08.324287Z","updated_at":"2026-10-14T17:03:04.863136Z","company_name":"Distyl AI","company_slug":"distyl-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=distyl.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ebabc8b3-5891-442b-a4d0-7a3ac1388c2d"},{"id":"bfd991d3-7f62-425c-bcfb-f01c579b3fb5","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, Data and AI Platform","slug":"software-engineer-data-and-ai-platform-a47789b0","description":"DoorDash is building the world's most reliable on-demand, logistics engine for delivery! We're looking for talented engineers to help us develop a 24x7, global infrastructure system that powers DoorDash's three-sided marketplace of consumers, merchants, and dashers.\n About the Team \n Data Platform’s Data User Experience Engineering builds the products every DoorDash employee uses to answer questions with data. That portfolio includes our AI first reporting and authoring surface,Conversational analytics with AI, Semantic and Knowledge layer along with MCP and skills layer plus the full stack infrastructure that lets users and agents reach all of it. Our users span analysts, engineers, operators, and executives across DoorDash, Wolt, and Deliveroo, on a warehouse and OLAP footprint that includes Snowflake,Spark and Clickhouse. The team is a mix of engineers who have built AI \u0026 Data platforms and products at scale.\n About the Role \n You will own the full path from a question to an answer an operator can put in a deck without checking it twice. The BI reporting surfaces are converging, and deciding what that convergence looks like while someone is authoring, building a report, writing a query, asking why a number moved, or asking for the chart they were about to build by hand.\n You’re excited about this opportunity because you will… \n \n Ship agentic analytics features to thousands of users  where a wrong number is a real business decision made badly, and accuracy is the product\n Build a data platform for authoring, dashboard runtime, drill-down and cross-filter semantics, scheduling and alerting, content certification and permissions, and the embedding path that puts the same components in front of external users\n Streaming large result sets to the browser, rendering dozens of interdependent tiles.\n Build the AI experience inside that platform: agent-assisted query authoring, generated charts and widgets held to the same governance as hand-built ones\n Evolve the semantic layer  metric and dimension modeling, definition governance, lineage  and expose metadata in forms a model can actually reason over\n Own the query and adaptive caching path: planning and federation across different query engines and invalidation correctness, and safe execution under agent traffic that bursts and fans out\n Develop AI-enabled services and workflows, including self-serve tooling for creating, evaluating, and improving AI assistants\n Build and operate data discovery and metadata platforms that help users find the right datasets, dashboards, and metrics quickly and confidently\n \n We're excited about you because you have… \n \n B.S., M.S., or PhD. in Computer Science or equivalent\n 3+ years of industry experience in software engineering\n Strong backend fundamentals, especially in Go,Python and the ability to own a service in production \n Shipped an LLM-powered feature to real users, hands-on with retrieval and grounding and built or meaningfully extended an eval system\n Built meaningful pieces of data platform products- semantic layers, data discovery, reporting applications, caching systems and data applications\n Familiarity with a cloud-based environment such as AWS and with Kubernetes in production\n Experience with MCP, agent frameworks, or tool-calling architectures in production\n Experience with traditional and modern reporting or analytics tooling, data governance, controls and multi-tenant data platforms\n Prompt and context optimization at scale -  caching, token budgeting, cost/latency tuning\n Full stack experience is a plus\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["fine-tuning","llm","cloud","healthcare","computer-graphics","agents"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8192187","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T20:21:21Z","expires_at":"2026-11-13T17:03:28.751716Z","created_at":"2026-10-14T15:25:31.393614Z","updated_at":"2026-10-14T17:03:28.754637Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bfd991d3-7f62-425c-bcfb-f01c579b3fb5"},{"id":"1d5ba28a-37f9-4138-9c77-647324c5d234","company_id":"b6e5a3d1-9bde-4a82-8d78-9f38ed99ee81","title":"Research Manager - Post-Training for Agents","slug":"research-manager-post-training-for-agents-5de82862","description":"As a Research Manager within Datadog AI Research (DAIR), you will lead a team working on fundamental research in post-training and autonomous agents while remaining deeply engaged in the technical direction of the work. You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will provide technical leadership to a team of researchers and research engineers, mentor and develop highly technical talent, shape research direction, and collaborate with Datadog’s product and engineering teams to translate research advances into products. This role is designed for an exceptional researcher or technical leader who wants to maintain a strong hands-on research identity while developing and leading a high-performing team.  \n At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.  \n What You'll Do:  \n \n Lead research in agent post-training and reinforcement learning, setting the technical direction for ambitious research programs grounded in observability and security\n Guide and mentor researchers and research engineers while remaining closely involved in research strategy, experimentation, model development, and technical problem-solving \n Design and advance reinforcement learning approaches, post-training methods, and training loops for agents operating in complex environments \n Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement\n Collaborate with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities\n Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks \n \n Who You Are:  \n \n You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling \n You have demonstrated the ability to lead technically ambitious research at meaningful scale, whether in an industry research lab, startup, academic environment, or another research setting \n You have extensive hands-on experience designing or implementing reinforcement learning systems, agent training methods, simulated environments, synthetic data pipelines, or evaluation frameworks\n You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements \n You have operated at a senior technical level and have experience leading, mentoring, and developing teams of researchers or engineers, while remaining deeply hands-on in technical direction and execution \n You want to develop and support a research team while maintaining a strong technical identity, and you can communicate complex research findings effectively across technical and non-technical audiences \n \n Datadog values people from all walks of life. We know not everyone will meet all the above qualifications on day one. That’s okay. If you’re passionate about technology and want to grow your experience, we encourage you to apply.  \n Benefits and Growth:  \n \n Competitive global benefits \n New hire stock equity (RSUs) and employee stock purchase plan (ESPP)\n Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris \n Opportunity to attend and present at conferences and meetups \n Intra-departmental mentor and buddy program for in-house networking\n An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)  \n \n Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. \n #LI-Hybrid\n Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.\n The reasonably estimated yearly salary for this role at Datadog is:\n $244,000 — $305,000 USD \n \n About Datadog:  \n Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applic","salary_min":244000,"salary_max":305000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","search","agents","healthcare","data-pipeline","research"],"apply_url":"https://careers.datadoghq.com/detail/8279173/?gh_jid=8279173","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:57:23Z","expires_at":"2026-11-13T16:55:38.246957Z","created_at":"2026-10-14T15:19:28.467785Z","updated_at":"2026-10-14T16:55:38.249824Z","company_name":"Datadog","company_slug":"datadog","company_logo_url":"https://www.google.com/s2/favicons?domain=datadoghq.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1d5ba28a-37f9-4138-9c77-647324c5d234"},{"id":"b34f4013-d975-493b-8a19-92a0a531c511","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Software Engineer, Machine Learning","slug":"staff-software-engineer-machine-learning-c84454fb","description":"About the Team \n DoorDash is building the world's most reliable on-demand, logistics engine for delivery. We are continuing to grow rapidly and expanding our Engineering offices globally! We are looking for Software Engineers, Machine Learning to build and maintain a large scale 24x7 global infrastructure system that powers DoorDash's 3-sided marketplace of Consumers, Merchants and Dashers.\n About the Role \n As a Software Engineer, Machine Learning, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the catalog system and our product knowledge graph at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make our product knowledge graph accurate, standardized, semantically rich, easily discoverable, and extensible. We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multi-disciplinary teams.\n This role is hybrid with some in-office time expected and will report to an Engineering Manager. \n You're excited about this opportunity because you will... \n \n Develop advanced machine learning models to improve ads efficiency and quality.\n Design and build optimization algorithms for budget pacing and automated bidding to achieve various advertising goals. \n Establish a data-driven framework to understand how the bid density and market competitiveness would affect advertising value and platform revenue. \n Develop new data solutions (eg. embeddings and consumer profiles) to target the relevant audience.\n Be responsible for the end-to-end ML lifecycle, including ideation, offline model training, online shadowing/deployment, experimentation, and post-launch monitoring/measurement.\n Build and extend the current data/ML infrastructure to empower Ads data applications including data analysis, ML modeling, and experimentation.\n Scale our systems and services to fuel the growth of our business.\n \n We're excited about you because you have... \n \n M.S., or PhD. in a technical field such as computer science, mathematics, statistics, physics or equivalent.\n 8+ years ML industry experience with a solid understanding of machine learning algorithms and fundamentals.\n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n Experience building data/feature engineering pipelines at scale using Pyspark and Snowflake SQL.\n Experience with building machine learning systems in production by using frameworks such as PyTorch, Keras, LightGBM, scikit-learn, Spark ML, or related.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $203,500 — $299,300 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","cloud","healthcare","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8092262","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:49:36Z","expires_at":"2026-11-13T17:03:28.820143Z","created_at":"2026-10-14T15:25:31.461436Z","updated_at":"2026-10-14T17:03:28.823202Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b34f4013-d975-493b-8a19-92a0a531c511"},{"id":"2bdabd0b-9451-4388-b502-2ba7d839c43f","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Machine Learning Engineer, Drive","slug":"senior-machine-learning-engineer-drive-202e134e","description":"About the Team\n DoorDash Drive powers deliveries placed through merchants' own channels—including their websites, mobile apps, and phone orders—using DoorDash's logistics network. The Drive Machine Learning team builds the prediction and intelligence systems that power this business, including delivery and pickup time estimation, merchant prep-time prediction, order release optimization, logistics decision-making, and AI-powered delivery quality signals. Drive presents a unique machine learning challenge. Every merchant has different operational workflows, preparation patterns, and customer expectations, requiring models that generalize across millions of deliveries while adapting to highly diverse merchant behavior. Our team has significant opportunities to improve prediction accuracy, optimize logistics decisions, and build AI-native experiences that directly improve merchant, consumer, and dasher outcomes.\n About the Role\n As a Machine Learning Engineer on the Drive team, you'll own machine learning systems end-to-end—from feature engineering and model development to experimentation, deployment, monitoring, and continuous iteration. Your work will span several high-impact problem areas:\n \n Build next-generation machine learning models for delivery ETA, pickup ETA, merchant prep-time estimation, and order release prediction that improve reliability for merchants and consumers.\n Develop deep learning models that leverage large-scale spatiotemporal, marketplace, and behavioral signals to improve prediction accuracy.\n Apply reinforcement learning and optimization techniques to improve logistics decision-making, assignment strategies, and marketplace efficiency.\n Build AI-native product experiences using large language models (LLMs) and vision-language models (VLMs). For example, transform pickup photos, item verification flows, receipts, and drop-off images into structured quality signals that help verify orders, prevent delivery defects, and improve issue resolution.\n Design and run rigorous online experiments, production monitoring, and model iteration to continuously improve performance.\n Partner closely with software engineers, product managers, data scientists, and platform teams to bring new machine learning capabilities into production at scale.\n You'll have the opportunity to work across traditional machine learning, deep learning, reinforcement learning, optimization, and multimodal AI while solving some of the most challenging logistics problems at DoorDash.\n \n You're excited about this opportunity because... \n \n You enjoy solving large-scale machine learning problems that directly impact millions of deliveries.\n You have a strong sense of ownership and enjoy taking models from research through production.\n You're comfortable working in ambiguous environments where experimentation and iteration drive product decisions.\n You care about both model quality and production reliability.\n You're excited to work across a diverse set of ML techniques—from neural networks and optimization to multimodal AI.\n You collaborate well across engineering, product, and data science teams.\n \n We’re excited about you because… \n \n 5+ years of industry experience building and shipping production machine learning systems with measurable business impact (Bachelor's, Master's, or PhD).\n Strong experience developing production machine learning models using modern deep learning frameworks such as PyTorch and distributed data processing technologies such as Spark and Airflow.\n Experience building, deploying, monitoring, and maintaining production ML systems end-to-end.\n Strong software engineering skills in Python and experience with modern ML infrastructure and tooling.\n Deep expertise in at least one of the following areas:\n \n Deep Learning\n Reinforcement Learning\n Optimization / Operations Research\n Large Language Models (LLMs) or Vision-Language Models (VLMs)\n \n Experience applying machine learning to estimation, ranking, prediction, optimization, or decision-making problems at production scale.\n Hands-on experience with LLMs or VLMs is a strong plus.\n Experience in logistics, marketplaces, or delivery platforms is helpful but not required.\n Proficiency using AI-assisted development tools (e.g. Claude Code, Codex, Cursor) throughout the software development lifecycle.\n You are located or are planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA.\n Compensation \n The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions.  Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about yo","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","deep-learning","reinforcement-learning","cloud","llm","generative-ai","healthcare","pytorch"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8221807","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:42:26Z","expires_at":"2026-11-13T17:03:28.706911Z","created_at":"2026-10-14T15:25:31.350903Z","updated_at":"2026-10-14T17:03:28.710264Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2bdabd0b-9451-4388-b502-2ba7d839c43f"},{"id":"78fae61b-8780-438a-b75a-b7c5778b3828","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Software Engineer, Machine Learning","slug":"senior-software-engineer-machine-learning-851e39b8","description":"About the Role \n As a Senior Software Engineer, Machine Learning, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the catalog system and our product knowledge graph at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make our product knowledge graph accurate, standardized, semantically rich, easily discoverable, and extensible. We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multi-disciplinary teams. We expect Senior Engineers to be able to own projects end-to-end and mentor their junior and mid level peers.\n This role is hybrid with some in-office time expected and will report to an Engineering Manager. \n You're excited about this opportunity because you will... \n \n Develop advanced machine learning models to improve core parts of your teams scope.\n Be responsible for the end-to-end ML lifecycle, including ideation, offline model training, online shadowing/deployment, experimentation, and post-launch monitoring/measurement.\n Work on creating novel ML \u0026 AI solutions that push both our business and the industry forward.\n Scale our systems and services to fuel the growth of our business.\n Completely disrupt logistics by tackling bleeding-edge, technical problems\n \n We're excited about you because you have... \n \n M.S., or PhD. in a technical field such as computer science, mathematics, statistics, physics or equivalent.\n 5+ years ML industry experience with a solid understanding of machine learning algorithms and fundamentals.\n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n Experience building data/feature engineering pipelines at scale using Pyspark and Snowflake SQL.\n Experience with building machine learning systems in production by using frameworks such as PyTorch, Keras, lightgbm, scikit-learn, Spark ML, or related.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $167,800 — $246,800 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods. DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.\n Our Commitment to Diversity and Inclusion\n We’re committed to growing and empowering a more inclusive","salary_min":167800,"salary_max":246800,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","healthcare","pytorch","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8143205","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:38:25Z","expires_at":"2026-11-13T17:03:28.716694Z","created_at":"2026-10-14T15:25:31.359543Z","updated_at":"2026-10-14T17:03:28.719518Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/78fae61b-8780-438a-b75a-b7c5778b3828"},{"id":"9b894a7b-e6ce-485c-b095-970a736af6dc","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Machine Learning Engineer, New Verticals Agentic Foundations","slug":"senior-machine-learning-engineer-new-verticals-agentic-foundations-a3f3a5d5","description":"About the Team\n Agentic Foundations, part of New Verticals ML, builds the core AI/ML intelligence that powers high-quality, scalable agents at DoorDash, and proves it through flagship agent experiences such as the Ask grocery assistant. We operate as a closed loop: new models, evals, and representations make our agents better, and real-world agent usage surfaces the gaps that drive our next foundations work. \n About the Role\n We’re looking for a Senior Machine Learning Engineer to join Agentic Foundations with a primary focus on Vertical Agent Development. You will own the continuous improvement of our flagship agents, starting with the Ask Assistant, using an eval-driven loop to make them faster, more cost-efficient, and more reliable for millions of customers. You will also help us bring this approach to new agentic experiences for Merchants and Dashers, turning promising ideas into production-ready agents.\n This is a chance to work at the intersection of LLMs, agents, post-training, and evals on problems that ship. You will partner closely with engineers working on agentic memory, agent-compatible product representations, and post-training of small language models (SLMs), so that what we learn from production agents feeds directly back into our foundations, and what we build there makes our agents better.\n You will report into the engineering lead on our New Verticals AI/ML team. We expect this role to be hybrid with some time in-office and some time remote.\n You’re excited about this opportunity because you will…\n \n Raise the quality of our Consumer agents: build and refine our eval harness-optimization loop to pinpoint failure modes, then iterate on prompts, tools, skills, and context, and measure the impact on real customer tasks.\n Find the best balance of quality, latency, and cost: run rigorous experiments, including fine-tuning open-weights models and deploying small language models (SLMs) where they can replace or augment larger LLMs.\n Launch new agents: take agentic opportunities for Merchants and Dashers from early exploration to production.\n Turn foundations into product impact: work with teammates on agentic memory, agent-compatible product representations, and post-training SLMs for steerable generative recommendation, and bring those capabilities into production agents.\n Shape the roadmap: partner with engineering, product, and business leaders to define an ML-driven strategy for our fast-growing grocery and retail delivery business.\n \n We’re excited about you because you have…\n \n 3+ years of industry ML experience, including hands-on work building and shipping LLM-based agents (tool use, context management, prompting, guardrails) and improving them with evals and data\n Experience with post-training or fine-tuning of open-weights models (e.g., SFT, preference optimization, or RL), ideally including small language models, and sound judgment on quality, latency, and cost trade-offs\n Strong foundation in NLP and machine learning, with proficiency in Python and frameworks such as PyTorch or TensorFlow\n M.S. or PhD in Computer Science, Statistics, Math, or another quantitative field, or equivalent practical experience, plus a collaborative, growth-minded approach and a drive for measurable impact\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United","salary_min":167800,"salary_max":246800,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["tensorflow","llm","fine-tuning","agents","cloud","pytorch","nlp","healthcare"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8279145","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:38:05Z","expires_at":"2026-11-13T17:03:28.711787Z","created_at":"2026-10-14T15:25:31.355292Z","updated_at":"2026-10-14T17:03:28.714838Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9b894a7b-e6ce-485c-b095-970a736af6dc"},{"id":"634e56a9-b38d-474b-847e-3dc417b38339","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Personalization","slug":"staff-machine-learning-engineer-personalization-d2d9e9a7","description":"About the Team \n Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop modern growth and personalization models that power DoorDash's growing retail and grocery business.\n About the Role \n We’re looking for a passionate Applied Machine Learning expert to join our team. As a Staff Machine Learning Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the growth and personalization experiences at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories. You will demonstrate a strong command of production level machine learning,  experience with solving end-user problems, and collaborate well with multi-disciplinary teams.\n You will report into the engineering manager on our Personalization team. We expect this role to be hybrid with some time in-office and some time remote (#LI-Hybrid).\n You’re excited about this opportunity because you will… \n \n Develop production machine learning solutions to build a world class personalized shopping experience for a diverse and expanding retail space\n Partner with engineering and product leaders to help shape the product roadmap applying ML\n Mentor junior team members, and lead cross functional pods to create collective impact\n \n You can find out more on our ML blog here \n We’re excited about you because you have… \n \n 8+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. \n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field\n Expertise in applied ML for  Causal Inference and Recommendation Systems  -  both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms \u0026 LLMs is a plus.  \n Machine learning background in Python; experience with PyTorch or TensorFlow preferred.\n Ability to communicate technical details to nontechnical stakeholders\n You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down\n Desire for impact with a growth-minded and collaborative mindset\n \n \n Data Retention, Privacy \u0026 Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our Careers Page . \n Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only\n We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.\n The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey \n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","cloud","deep-learning","fine-tuning","healthcare","pytorch","tensorflow","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8225006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:31:26Z","expires_at":"2026-11-13T17:03:28.796671Z","created_at":"2026-10-14T15:25:31.438732Z","updated_at":"2026-10-14T17:03:28.799759Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/634e56a9-b38d-474b-847e-3dc417b38339"},{"id":"501a4fae-853d-480b-9dbb-39daa44f8047","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Fulfillment Planning","slug":"staff-machine-learning-engineer-fulfillment-planning-53570b60","description":"About the Team \n The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. We optimize how deliveries are planned and executed across the full delivery lifecycle, improving customer experience, merchant outcomes, Dasher efficiency, and DoorDash profitability.  Our mission is to improve fulfillment quality while reducing fulfillment cost. We do this by applying machine learning, optimization, and systems engineering to the core decisions behind assignment, routing, batching, timing, and fulfillment estimation.\n The team works on some of DoorDash’s most important logistics systems, including:\n \n The core assignment engine that matches deliveries with Dashers in real time.\n Real-time ETA and fulfillment estimation systems for consumers, Dashers, and merchants across diverse geographies and all business lines.\n Assignment and planning algorithms for specialized delivery types, including grocery, retail, parcel, and catering.\n ML models and optimization algorithms that shape demand, improve service quality, and reduce cost.\n Tier-0 logistics services that require high reliability, low latency, and strong operational discipline.\n \n The team also builds reusable ML systems and modeling patterns that scale across DoorDash’s logistics ecosystem. This role will help define the technical direction and best practices for logistics ML at DoorDash.\n About the Role \n We’re looking for a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems that drive real-time decisioning across DoorDash’s fulfillment ecosystem.\n You will start by owning ML systems for assignment and fulfillment estimation, partnering closely with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency. Over time, you may also contribute to adjacent areas such as batching, fulfillment execution, demand shaping, and logistics optimization across DoorDash’s business lines.\n This is a high-impact individual contributor role for someone who enjoys building 0→1 ML systems, operating at Staff-level scope, and influencing technical direction across multiple teams. You will define architectures, set modeling and deployment standards, mentor other engineers, and help shape how DoorDash applies machine learning to logistics at scale.\n You’re excited about this opportunity because you will… \n \n Own and build foundational ML systems that directly impact delivery quality, cost, and overall logistics efficiency across DoorDash.\n Work on challenging, real-world machine learning problems , including real-time assignment, routing, and fulfillment estimation.\n Lead 0→1 ML initiatives , defining how machine learning and optimization are applied across fulfillment products.\n Influence architecture, strategy, and execution for a Tier-0 service critical to DoorDash’s logistics platform.\n Collaborate closely with Product, Data Science, and Platform Engineering in a highly cross-functional environment.\n Establish best practices for model development, deployment, monitoring, retraining, and governance.\n Define and lead DoorDash’s cutting-edge AI vision for logistics: an LLM-inspired foundation model for intelligence across logistics\n Mentor other engineers and raise the technical bar for logistics ML across the organization.\n \n We’re excited about you because… \n \n You have 8+ years of industry experience building and deploying production-scale machine learning systems.\n You have strong machine learning fundamentals and know how to apply them to large-scale production systems.\n You are fluent in Python\n You have hands-on experience with modern ML frameworks, especially deep learning frameworks.\n You have designed, launched, and operated mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance.\n You can lead complex technical projects end to end and influence stakeholders across multiple teams or organizations.\n You communicate clearly with both technical and non-technical audiences.\n You are comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems.\n You have built or shipped large-scale ML models for recommendation, ads, marketplace, logistics, or other domains.\n You have experience with knowledge distillation from large teacher models into efficient production models.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares ","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","healthcare","cloud","generative-ai","llm","deep-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8224336","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:30:37Z","expires_at":"2026-11-13T17:03:28.79242Z","created_at":"2026-10-14T15:25:31.434336Z","updated_at":"2026-10-14T17:03:28.795093Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/501a4fae-853d-480b-9dbb-39daa44f8047"},{"id":"6aceb6c9-aa17-4cd3-81ca-d26284538246","company_id":"5b08d89d-9846-4ef7-8b53-d07ef3ee6c19","title":"Software Engineer, ML Infrastructure","slug":"software-engineer-ml-infrastructure-f2fd5868","description":"About Arc Institute \n Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.\n Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.\n Our two Institute Initiatives reflect this model in action:\n \n Virtual Cell Initiative : Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets,  accelerating the path from biological insight to clinical trials.\n Alzheimer's Disease Initiative : Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.\n \n More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.\n About the position \n You'll join Arc's Infrastructure Engineering team and own the data and evaluation of ML infrastructure for Arc's virtual cell models. You'll start with two systems: our Model Performance Dashboard, which benchmarks those models, and the pipeline that turns single-cell data into their training sets. You'll set the technical direction for both and connect them, so every new dataset version is judged by what it does to the models. Later in your first year, the role widens into the engineering that makes the ML team's training and evaluation run fast and at scale, lab-in-the-loop infrastructure, validation frameworks for agent-written software, and Arc's rules for what research data we bring in, keep and share. The role consolidates ownership that is split across several teams today. \n Why this position could be the best job you’ve ever had \n \n You will contribute to building a virtual cell. Your evals show which data makes Arc's models better, and the answer shapes which experiments Arc's labs run next.\n You will own the platform end to end, and you work directly with the ML researchers and scientists who rely on it.\n \n About you \n \n You finish what you own, and you fix what's broken without being asked.\n You're curious about the biology, enough to learn single-cell perturbation data and argue about what an eval should measure. \n You're steady when priorities shift. Research is high-risk, so you test ideas early and change course when the evidence says to.\n You don't wait on scarce expert time. If proving a dataset's value means running the model evals yourself, you run them.\n \n In this position you will \n \n Own how Arc measures the performance of its virtual cell models, starting with the backend of our Model Performance Dashboard, which you'll take over within your first two months. \n Decide with the ML team which evaluation tools they need, then design and build them so researchers can evaluate and compare models themselves, and measure what each dataset version does to model performance.\n Redesign the pipeline that turns single-cell data into training sets for our virtual cell models, then run it with AI agents: agents find, process and curate datasets, and automated quality checks, not manual review, decide whether a dataset version is ready for model training.\n Own the lab-in-the-loop infrastructure that turns model performance results into the case for what Arc's labs should test next, working directly with the science leads who design those experiments . \n Build or buy validation frameworks (tests, evals, QC gates, policies) that let agent-written code and apps ship without line-by-line human review. Start with our team, where you'll also own how we work with coding agents, then extend them to apps that scientists across Arc build themselves.\n Own the engineering that makes the ML team's training and evaluation run fast, reliably and at scale. The ML team owns the research and the models. Once a model is ready to push, it enters the train-and-eval CI system you own, which feeds our Model Performance Dashboard. You'll also make multi-node training a supported path, track down distributed-training failures, and measure and raise throughput and GPU utilization (MFU) on clusters that Arc IT operates.\n \n Requirements \n \n Bachelor’s degree with 8 years of relevant experience, or Master’s degree with 6 years of relevant experience.\n You've owned a production data or ML platform that researchers depend on, end to end (pipelines, storage, versioning), and set its technical direction.\n You design and build systems with AI tools and agents, and you make their output verifiable with evals, QC gates or tests before anyone relies on it.\n ","salary_min":191000,"salary_max":237000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","healthcare","pytorch","distributed-systems","agents","machine-learning","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/arcinstitute/jobs/6222681004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T22:16:26Z","expires_at":"2026-11-13T17:03:53.841883Z","created_at":"2026-10-14T15:25:57.647484Z","updated_at":"2026-10-14T17:03:53.845803Z","company_name":"Arc Institute","company_slug":"arc-institute","company_logo_url":"https://www.google.com/s2/favicons?domain=arcinstitute.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6aceb6c9-aa17-4cd3-81ca-d26284538246"},{"id":"9f3b22a7-0295-41b4-9648-e40148bbd237","company_id":"5b08d89d-9846-4ef7-8b53-d07ef3ee6c19","title":"Software Engineer, Lab Platform","slug":"software-engineer-lab-platform-eec06b9e","description":"About Arc Institute \n Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.\n Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.\n Our two Institute Initiatives reflect this model in action:\n \n Virtual Cell Initiative : Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets,  accelerating the path from biological insight to clinical trials.\n Alzheimer's Disease Initiative : Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.\n \n More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.\n About the position \n You'll join Arc's Infrastructure Engineering team and own support and operations for our laboratory information management system (LIMS). Arc's scientists record their samples and experiments there, including the variables they change from one experiment to the next. That record is business intelligence for the lab: it's how scientists learn which conditions improve an assay, and it also feeds the pipelines that process lab data automatically. You'll run the request queue, make schema changes, train users and maintain the checks on data as it's entered, and you'll turn the requests that keep coming back into automation. You don't need LIMS experience: strong Python and SQL and an interest in lab data matter more, and we'll teach you the platform. You'll start under our lab platform Tech Lead, who will review your work and mentor you.\n Why this position could be the best job you’ve ever had \n \n Your work reaches the lab within a week, and it shows up in results: the experiment records you keep accurate are how labs find what makes their assays perform better.\n You'll learn how a research institute turns lab work into data, with a Tech Lead whose goal is to hand you more of the system as you grow.\n \n About you \n \n You finish what you own, and you fix what's broken without being asked.\n You're curious about the biology and the mission. You want to know what a guide or a 10x Flex run is.\n You go to the scientists. You sit down with them, learn how they run and record their experiments, and understand what they need before you propose a fix.\n You're careful and thoughtful. You want more scope and you'll take it on before you have every skill it needs, and you double-check changes other teams depend on.\n You see which requests need a general solution, and you balance that work with the rest of the queue.\n \n In this position you will \n \n Run the request queue for Arc's lab data systems, from questions and fixes to schema changes, which you'll ship through our change workflow so other teams' pipelines don't break.\n Support the templates scientists record experiments with, and the command-line tools that pull their data for analysis.\n Run training sessions for scientists, and write the docs they come back to.\n Build and maintain the checks that catch errors in data as scientists enter it, so bad records don't reach analysis or pipelines.\n Help carry out the refactor of the team and permission structure in our LIMS, which decides who can see and edit what.\n Turn the requests that keep coming back into automation, and choose the approach that fits the problem: a script, a scheduled job, a template change or an AI agent. Write the runbook, add the checks and retire the manual step; changes go through the team's code review.\n By the end of your first year you will\n Build tools that make screen and experiment metadata queryable across systems for computational biologists and lab teams.\n Run the request queue and ship schema changes on your own, with the Tech Lead reviewing designs instead of each change.\n Own a part of the lab platform from design through support.\n \n Requirements \n \n Bachelor’s degree with 0-2 years of relevant experience, or an advanced degree.\n You write solid Python and SQL, and you're comfortable working with REST APIs.\n You debug problems in systems you didn't build, working from the code, logs and data.\n You use AI coding tools day to day or are ready to learn, you check what they produce, and you're careful with sensitive research data.\n You explain technical things clearly to scientists, and you write docs and training that people use.\n You ask for help early and take feedback well.\n You've owned a piece of work from start ","salary_min":118000,"salary_max":146000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","healthcare","agents","api-design"],"apply_url":"https://job-boards.greenhouse.io/arcinstitute/jobs/6222737004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T22:16:12Z","expires_at":"2026-11-13T17:03:53.836624Z","created_at":"2026-10-14T15:25:57.643168Z","updated_at":"2026-10-14T17:03:53.839898Z","company_name":"Arc Institute","company_slug":"arc-institute","company_logo_url":"https://www.google.com/s2/favicons?domain=arcinstitute.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9f3b22a7-0295-41b4-9648-e40148bbd237"},{"id":"362dd4a4-f865-4d4a-9a7a-bc7e1b3dff50","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Staff Machine Learning and Bioinformatics Scientist (Multi-Cancer Early Detection)","slug":"staff-machine-learning-and-bioinformatics-scientist-multi-cancer-early-detection-df2fad4b","description":"Natera’s Research team is building tests that can detect many cancers from a single blood draw. We are looking for a Staff Machine Learning and Bioinformatics Scientist to help design the models at the heart of that effort. You’ll work with large-scale, multi-modal genomic datasets from clinical cohorts and partner closely with molecular biologists and clinical scientists. Your research will advance the state-of-the-art and you will drive new methods from prototype, through scalable implementation, toward clinical validation and, ultimately, to patients.\n PRIMARY RESPONSIBILITIES: \n \n Lead the development of novel statistical and machine learning methods for cancer signal detection on multi-modal genomic data\n Build interpretable, well-calibrated models, setting the standard for uncertainty estimation and reproducibility across the group\n Prototype, test and productionize ML models using scalable infrastructure\n Own key technical deliverables within larger projects and help shape the team’s research roadmap\n Collaborate closely with molecular biologists and clinical experts on design and analysis of experiments and validation studies\n Keep raising the bar through code and design reviews across projects\n Translate complex results into clear stories for technical and non-technical audiences\n Mentor junior scientists and champion a culture of curiosity, collaboration, and scientific excellence\n \n QUALIFICATIONS: \n \n PhD in Computer Science, Mathematics, Bioinformatics, or a related quantitative field with 6-12 years of experience post-PhD\n Hands-on experience developing novel statistical / ML analysis methods applied to complex biological data\n Proven track record of innovation and technical leadership in complex method development projects\n Expertise in deep learning and multimodal foundation models\n Proficient in Python and its scientific computing ecosystem\n Scientific rigor throughout the R\u0026D process, with eagerness to teach and learn new computational methods and biology\n Excellent communication skills and a collaborative approach across teams\n \n Preferred Experience: \n \n Familiarity with DNA methylation and other epigenomic data\n Experience with cell-free DNA and liquid biopsy samples\n Background in cancer genomics and diagnostics\n Hands-on work with multimodal foundation models applied to biological data\n The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years \u0026 depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.\n Remote USA\n $163,200 — $204,000 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.\n The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.\n WHAT WE OFFER \n Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!\n For more information, visit www.natera.com .\n Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide.\n All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability or any other legally protected status. We also consider qualified applicants regardless of criminal histories, consistent with applicable laws.\n If you are based in California, we encourage you to read this important information for California residents.  \n Link: https://www.natera.com/notice-of-data-collection-california-residents/ \n Please be","salary_min":163200,"salary_max":204000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["generative-ai","deep-learning","healthcare","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6220885004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:10:48Z","expires_at":"2026-11-13T16:58:38.001158Z","created_at":"2026-10-14T15:22:15.63645Z","updated_at":"2026-10-14T16:58:38.003762Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/362dd4a4-f865-4d4a-9a7a-bc7e1b3dff50"},{"id":"51543c6e-0d4b-4d56-b403-d772dc9cc157","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Staff Machine Learning and Bioinformatics Scientist (Multi-Cancer Early Detection)","slug":"staff-machine-learning-and-bioinformatics-scientist-multi-cancer-early-detection-bbaf4d4a","description":"Natera’s Research team is building tests that can detect many cancers from a single blood draw. We are looking for a Staff Machine Learning and Bioinformatics Scientist to help design the models at the heart of that effort. You’ll work with large-scale, multi-modal genomic datasets from clinical cohorts and partner closely with molecular biologists and clinical scientists. Your research will advance the state-of-the-art and you will drive new methods from prototype, through scalable implementation, toward clinical validation and, ultimately, to patients.\n PRIMARY RESPONSIBILITIES: \n \n Lead the development of novel statistical and machine learning methods for cancer signal detection on multi-modal genomic data\n Build interpretable, well-calibrated models, setting the standard for uncertainty estimation and reproducibility across the group\n Prototype, test and productionize ML models using scalable infrastructure\n Own key technical deliverables within larger projects and help shape the team’s research roadmap\n Collaborate closely with molecular biologists and clinical experts on design and analysis of experiments and validation studies\n Keep raising the bar through code and design reviews across projects\n Translate complex results into clear stories for technical and non-technical audiences\n Mentor junior scientists and champion a culture of curiosity, collaboration, and scientific excellence\n \n QUALIFICATIONS: \n \n PhD in Computer Science, Mathematics, Bioinformatics, or a related quantitative field with 6-12 years of experience post-PhD\n Hands-on experience developing novel statistical / ML analysis methods applied to complex biological data\n Proven track record of innovation and technical leadership in complex method development projects\n Expertise in deep learning and multimodal foundation models\n Proficient in Python and its scientific computing ecosystem\n Scientific rigor throughout the R\u0026D process, with eagerness to teach and learn new computational methods and biology\n Excellent communication skills and a collaborative approach across teams\n \n Preferred Experience: \n \n Familiarity with DNA methylation and other epigenomic data\n Experience with cell-free DNA and liquid biopsy samples\n Background in cancer genomics and diagnostics\n Hands-on work with multimodal foundation models applied to biological data\n \n Compensation \u0026 Total Rewards  \n This range reflects a good-faith estimate of the base pay we reasonably expect to offer at the time of  hire. Final compensation will vary based on experience, qualifications, and internal equity considerations. \n This position is also eligible for additional compensation and benefits through Natera’s robust Total Rewards program, including: \n \n \n Annual performance incentive bonus \n \n Long-term equity awards \n \n Comprehensive health benefits (medical, dental, vision) \n \n 401(k) with company match \n \n Generous paid time off and company holidays \n \n Additional wellness and work-life benefits \n \n \n Compensation Range \n $169,800 — $212,300 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.\n The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.\n WHAT WE OFFER \n Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!\n For more information, visit www.natera.com .\n Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide.\n All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability","salary_min":169800,"salary_max":212300,"location":"San Carlos, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","deep-learning","healthcare","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6221940004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:10:46Z","expires_at":"2026-11-13T16:58:37.997051Z","created_at":"2026-10-14T15:22:15.640367Z","updated_at":"2026-10-14T16:58:37.999729Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/51543c6e-0d4b-4d56-b403-d772dc9cc157"},{"id":"6cc9a0fc-3641-41f8-a1b2-ae663ebbe1ed","company_id":"a0000000-0000-0000-0000-000000000001","title":"Cybersecurity Sales Specialist","slug":"cybersecurity-sales-specialist-412149d2","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n AI is changing what security teams can do. Claude can find and help fix vulnerabilities in code at a pace that wasn't possible a year ago, and we want defenders to have that advantage first. As a Cybersecurity Sales Specialist, you'll own Anthropic's security business across the accounts in one of our business units, such as financial services, healthcare and life sciences, or technology. You'll lead the security strategy with CISOs, partnering with the Account Executives who own the accounts in your industry to drive opportunities and put Claude to work on their hardest security problems.\n You'll help decide how we sell security, what we learn from customers, and how that shapes our products. The work matters to our mission: a safe transition through transformative AI depends on the people who defend critical systems being able to use it well.\n Responsibilities: \n \n \n Build and close security business, from the first conversation with a CISO's team through contract and expansion, working with the Account Executive who owns each relationship\n \n Create security pipeline with the Account Executives in your industry, from their existing accounts and through outbound to security leaders, events and joint work with partners\n \n Lead with the customer's problem: finding and fixing vulnerabilities, application security, security operations, and deploying AI agents securely\n \n Partner with your Applied AI engineers on discovery, proofs of concept and architecture, and know when to bring them in\n \n Guide customers through security reviews, verification for security use cases, and procurement\n \n Teach the Account Executives in your business unit to spot and qualify security opportunities\n \n Bring what you hear from security teams back to our product, safeguards and marketing teams\n \n You may be a good fit if you: \n \n \n Have closed complex, multi-stakeholder enterprise deals where the security organization was the buyer or the deciding approver\n \n Have worked as a specialist alongside account teams while carrying your own number, and can say exactly what you did to win the deal\n \n Can hold credible conversations with a CISO, an application security lead and a security engineer about how their work gets done\n \n Have deep experience in at least one security domain, such as application security, vulnerability management, security operations, cloud, data, identity or endpoint security\n \n Build pipeline where there's no playbook yet, through outbound and partners rather than inherited accounts\n \n Lead through influence, not authority: Account Executives bring you into their accounts because you help them win\n \n Sell consultatively: you understand the problem before you propose anything, and you're honest about what a product can't do\n \n Care about making AI systems safe and beneficial\n \n Strong candidates may also have: \n \n \n Experience selling security in one of the industries we cover, and an understanding of how security is bought and regulated there\n \n Experience selling AI, developer or data platform products to technical buyers\n \n Hands-on experience as a security practitioner or sales engineer before moving into sales\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $380,000 — $450,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their ca","salary_min":380000,"salary_max":450000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["healthcare","security","alignment","agents"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5449091008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T19:39:44Z","expires_at":"2026-11-13T16:54:55.325851Z","created_at":"2026-10-14T15:17:21.599155Z","updated_at":"2026-10-14T16:54:55.328475Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6cc9a0fc-3641-41f8-a1b2-ae663ebbe1ed"},{"id":"e93a2a59-96bf-439e-aeab-74ec34d72a67","company_id":"a0000000-0000-0000-0000-000000000018","title":"Machine Learning Engineer, Trust \u0026 Safety","slug":"machine-learning-engineer-trust-safety-fc325068","description":"About Vercel:\n Vercel is the agentic infrastructure company, freeing people and agents to ship what's next. For more than a decade we've helped builders move from idea to production with speed, security, and exceptional developer experience. \n Now we're scaling our products for both agents and people to ship and run software, built in the open and trusted by OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide. \n About the Role:\n We are looking for a Machine Learning Engineer on our Trust \u0026 Safety Engineering team, to build and operate the production systems that detect and stop abuse on the platform at internet scale. This is an engineering-first role, roughly 80% writing and shipping production code (services, pipelines, and infrastructure) and 20% modeling, taking detection and classification approaches from prototype to hardened systems. This is a hybrid role based in San Francisco or New York City, with three days a week in the office.\n What you’ll do: \n \n Design, build, and operate production systems that detect and disrupt abusive behavior across the platform, with an emphasis on reliability, scalability, and observability\n Build and maintain machine learning (ML) infrastructure, including training pipelines, feature pipelines, serving infrastructure, and evaluation frameworks\n Ship large language model (LLM) and classical ML approaches for abuse detection and classification, turning prototypes into maintainable code\n Own systems end to end, from integrating a model into the platform through deployment, monitoring, and iteration\n Work with security, product, and infrastructure teams to turn abuse patterns and detection logic into production systems\n \n What you need: \n \n 5+ years of software engineering experience building and operating production systems at scale\n Wrote production code in Python, plus JavaScript/TypeScript or Go\n Built and maintained ML infrastructure, such as training and serving pipelines, feature stores, or evaluation tooling\n Integrated LLMs into production systems, including prompt engineering and evaluation\n \n Bonus if you: \n \n Built an open source ML tool\n Worked in the trust and safety, fraud, or abuse prevention domain\n \n Compensation \u0026 Benefits:\n \n Competitive compensation package, including equity.\n Inclusive Healthcare Package.\n Learn and Grow - we provide mentorship and send you to events that help you build your network and skills.\n Flexible Time Off.\n We will provide you the gear you need to do your role, and a WFH budget for you to outfit your space as needed.\n \n The San Francisco, CA base pay range for this role is $208,000.00 - $312,000.00. Actual salary will be based on job-related skills, experience, and location. Compensation outside of San Francisco may be adjusted based on employee location. The total compensation package may include benefits, equity-based compensation, and eligibility for a company bonus or variable pay program depending on the role. Your recruiter can share more details during the hiring process. \n \n Disclosures: \n \n Privacy: Please review our Job Applicant Privacy Policy for more information on how we handle your data.\n Equal Opportunity:  Vercel is committed to fostering and empowering an inclusive community within our organization. We do not discriminate on the basis of race, religion, color, gender expression or identity, sexual orientation, national origin, citizenship, age, marital status, veteran status, disability status, or any other characteristic protected by law. Vercel encourages everyone to apply for our available positions, even if they don't necessarily check every box on the job description.","salary_min":208000,"salary_max":312000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","healthcare","agents","machine-learning","rust"],"apply_url":"https://job-boards.greenhouse.io/vercel/jobs/6208037004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T04:59:47Z","expires_at":"2026-11-13T16:55:41.948472Z","created_at":"2026-10-14T15:19:47.726056Z","updated_at":"2026-10-14T16:55:41.951722Z","company_name":"Vercel","company_slug":"vercel","company_logo_url":"https://www.google.com/s2/favicons?domain=vercel.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e93a2a59-96bf-439e-aeab-74ec34d72a67"},{"id":"89d58d57-03e8-4684-83b2-3121d619b65f","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, ML Serving Platform","slug":"software-engineer-ml-serving-platform-c430ad40","description":"About the Team \n DoorDash’s ML Serving Platform delivers tens of millions of predictions per second , powering search, recommendations, advertising, delivery estimates, and logistics across DoorDash, Wolt, and Deliveroo.\n Our customers are modelers and engineering teams across our internal business verticals. We build self-serve infrastructure that empowers them to bring new models into production, adopt open source software and models, and expand what they can accomplish with machine learning at scale.\n We work at the intersection of a rapidly evolving open source ecosystem and demanding production workloads. With active customer demand and growing modeling ambitions, we’re advancing the infrastructure behind today’s predictions while building the capabilities that enable the next generation of ML innovation.\n About the Role \n You’ll help build the next generation of our ML serving platform, connecting request routing and online feature retrieval with model inference on CPU and GPU infrastructure. You’ll tackle challenging infrastructure problems involving latency, reliability, resource efficiency, and scale, and make those capabilities accessible through self-serve tools and workflows.\n Working alongside experienced platform engineers, you’ll own defined projects from technical design and implementation through testing, rollout, and production support. You’ll partner directly with internal teams to understand emerging modeling requirements, remove adoption barriers, and turn advances in open source technology into measurable production improvements.\n You’re excited about this opportunity because you will… \n \n Empower modelers through self-serve infrastructure. Build tools, APIs, and workflows that help internal teams deploy, configure, validate, and operate models independently, accelerating ML adoption across business verticals.\n Bring open source innovation into production. Evaluate and integrate evolving inference frameworks and enable open source models, translating promising capabilities into reliable, efficient services at scale.\n Solve demanding inference infrastructure problems. Improve the systems that route prediction requests, retrieve online features, and execute models on a platform serving tens of millions of predictions per second.\n Help evolve our disaggregated serving architecture. Build modular components that allow routing, feature retrieval, and model execution to evolve and scale independently as workloads and modeling requirements change.\n Improve Kubernetes deployment and autoscaling. Help workloads respond to changing traffic while meeting latency and availability requirements and using CPU and GPU resources efficiently.\n Make adoption and rollout easier. Build integrations, validation, and migration tooling that help teams adopt new serving capabilities and our unified global platform with confidence.\n Own performance and reliability in production. Use benchmarking, profiling, metrics, and tracing to identify bottlenecks; participate in on-call and improve automation and runbooks to make the platform easier to operate.\n \n We’re excited about you because… \n \n You have 2+ years of software engineering experience building and maintaining production services or infrastructure.\n You have strong computer science fundamentals and proficiency in a backend or systems programming language such as Java, Kotlin, Go, C++, or Python.\n You understand distributed systems fundamentals, including concurrency, networking, timeouts, failure handling, and performance trade-offs.\n You enjoy challenging infrastructure problems and can independently turn a defined problem into a technical design, tested implementation, and safe production rollout.\n You have experience debugging production systems and using operational data to improve reliability, performance, or cost.\n You care about the engineers using your platform and collaborate effectively with customers and partners to make complex infrastructure easier to adopt.\n You hold a degree in Computer Science or a related field, or have equivalent practical experience.\n \n Nice to Have \n \n Experience with ML inference infrastructure, online feature retrieval, or other latency-sensitive distributed services.\n Experience operating containerized workloads on Kubernetes, including deployment, resource management, or autoscaling.\n Experience building self-serve developer platforms, APIs, or automation that helps other engineers move faster.\n Experience integrating open source infrastructure or models and validating their behavior under production workloads.\n Familiarity with CPU/GPU performance profiling, inference runtimes, or model deployment workflows.\n \n Applications for this position are accepted on an ongoing basis\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work lo","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["mlops","distributed-systems","fine-tuning","cloud","gpu","healthcare","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8263726","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T14:17:36Z","expires_at":"2026-11-13T17:03:28.772861Z","created_at":"2026-10-14T15:25:31.414102Z","updated_at":"2026-10-14T17:03:28.775658Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/89d58d57-03e8-4684-83b2-3121d619b65f"},{"id":"d6ccab06-8408-4826-b3a1-259ba586f060","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, Machine Learning Platform - MDX","slug":"software-engineer-machine-learning-platform-mdx-445b9a46","description":"About the Team \n Come help us build the world's most reliable on-demand, logistics engine for delivery! We're bringing on talented engineers to help us create and maintain a 24x7, no downtime, global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers.\n About the Role \n At DoorDash, our Data Scientists and ML Engineers have the opportunity to dive into a wealth of delivery data to improve company-wide ML workflows such as Search \u0026 Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning. You will join a small team to build systems that empower efficient machine learning at scale. This is a hybrid opportunity in San Francisco, Sunnyvale or Seattle.\n You’re excited about this opportunity because you will…\n \n Build a world-class ML platform where models are developed, trained, and deployed seamlessly\n Work closely with Data Scientists and Product Engineers to evolve the ML platform as per their use cases \n You will help build high performance and flexible pipelines that can rapidly evolve to handle new technologies, techniques and modeling approaches\n You will work on infrastructure designs and solutions to store trillions of feature values and power hundreds of billions of predictions a day\n You will help design and drive directions for the centralized machine learning platform that powers all of DoorDash's business.\n Improve the reliability, scalability, and observability of our training and inference infrastructure.\n \n We’re excited about you because… \n \n B.S., M.S., or PhD. in Computer Science or equivalent\n Exceptionally strong knowledge of CS fundamental concepts and OOP languages\n 2+ years of industry experience in software engineering\n Prior experience building machine learning systems in production such as enabling data analytics at scale\n Prior experience in machine learning - you've developed and deployed your own models - even if these are simple proof of concepts\n Systems Engineering - you've built meaningful pieces of infrastructure in a cloud computing environment. Bonus if those were data processing systems or distributed systems\n \n Nice To Haves \n \n Experience with challenges in real-time computing\n Experience with large scale distributed systems, data processing pipelines and machine learning training and serving infrastructure\n Familiar with Pandas and Python machine learning libraries and deep learning frameworks such as PyTorch and TensorFlow\n Familiar with Spark, MLLib, Databricks,MLFlow, Apache Airflow, Dagster and similar related technologies.\n Familiar with large language models like GPT, LLAMA, BERT, or Transformer-based architectures\n Familiar with a cloud based environment such as AWS\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $130,600 — $192,000 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order fo","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["healthcare","pytorch","cloud","llm","deep-learning","distributed-systems","fine-tuning","tensorflow"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8264534","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T14:17:34Z","expires_at":"2026-11-13T17:03:28.767496Z","created_at":"2026-10-14T15:25:31.409011Z","updated_at":"2026-10-14T17:03:28.770438Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d6ccab06-8408-4826-b3a1-259ba586f060"},{"id":"a91d5a21-3259-47d5-8df4-e146e013bc58","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, Intern - Labs (Summer 2027)","slug":"software-engineer-intern-labs-summer-2027-48ca1a56","description":"About Labs \n DoorDash Labs is an independent team within DoorDash. We're hiring software engineer interns to work at the intersection of software engineering and robotics to solve key business problems with elegant technical solutions. If you have a passion for applying robotics solutions to a service loved by millions of people, then we want to talk to you! \n About the Teams \n We are hiring Software Engineer interns across our Perception, Autonomy Platform, and DoorDash Air Cloud (Foundry) team. Please review the team descriptions below to learn about each. Selected candidates will be notified of which team is interested in pursuing their candidacy. \n   Perception: The Perception team helps Dot understand the world through vision foundation models and VLMs, supported by lidar and radar for safety. We tackle rare scenarios, adverse weather, and advances in reinforcement learning and end-to-end driving to make deliveries faster and smoother. We're seeking exceptional engineers in machine learning, computer vision, and state estimation to turn cutting-edge research into profitable autonomous delivery. Autonomy Platform: The Autonomy Platform team is responsible for the software foundation that Dot runs on. We build a wide variety of software frameworks for ensuring the robot operates reliably at an ever-increasing scale. This includes everything from designing the robot's operating system, building petabyte-scale data processing pipelines in the cloud, and optimizing nanosecond-level compute of bleeding-edge AI models. We are systems-oriented thinkers and hands-on builders who are excited to advance the state of the art in autonomy. Doordash Air Cloud (Foundry): The Air Cloud team, also known as Foundry, builds the cloud software that powers autonomous delivery across DoorDash—from the moment an order is placed and deemed eligible for drone or robot delivery, through merchant handoff, to flight management.   \n About the Role \n We believe interns are essential to our mission of attracting and retaining top talent. During our 12-week Software Engineer internship, interns are completely immersed in their teams and will have the opportunity to develop, maintain, and ship products. As an intern, you will directly impact our business by collaborating with your team to solve real problems for our customers.\n Applicants must be authorized to work in the U.S. to be considered for this position. F-1 students are eligible if they are using Curricular Practical Training (CPT). DoorDash does not support students on J-1 visas for internships at this time.\n The 2027 internship program will be hosted in person at a Doordash Labs office in either San Francisco or Sunnyvale Labs. You will have an opportunity to select your preferred location(s) and team preferences during the application process.\n You’re excited about this opportunity because you will… \n \n Develop, maintain and ship technical elements with the support of your mentor, manager, and team members\n Act on feedback, coaching, and mentorship from your mentor and team members\n Actively learn about the elements to which you contribute\n Make a direct impact on our business by collaborating with your team to solve problems for our customers\n Participate in intern programming - Leadership Speaker series, social events – virtually and in-person\n Present your summer learnings during a final presentation at the end of the internship\n You are excited about an in person summer work experience \n \n We’re excited about you because… \n \n You are pursuing a B.S. or M.S. Degree in Computer Science or equivalent, have no more than 2 years of full-time work experience, and are graduating between Fall 2027 to Summer 2028.\n You are available for a May or June 2027 start date (Internships are only hosted during the Summer)\n You have experience working with databases (e.g., AWS, SQL, etc.) \n You have a solid understanding of algorithms and data structures\n You have proficient experience working with at least 1 object-oriented programming language (e.g., Python, Java, Kotlin, etc.)\n You have experience writing clean code, working with version control, and doing unit testing. \n \n Preferred but not required  \n \n At least 1 previous Software Engineering internship or equivalent relevant experience\n Curiosity about and experience using AI-powered tools to support software development, problem solving, and learning, with an understanding of how to evaluate outputs and use these tools responsibly\n Ability to analyze and improve efficiency, scalability, and stability of various systems\n Excited to develop, release and run large-scale web applications\n Experience with solutions for systems monitoring, live deployments, and continuous integration\n Experience with real-time technology problems\n Experience working with service-oriented architecture, writing APIs, and designing systems.\n Participation in project or research-based technical work, hackathons, technical conferences/orga","salary_min":107400,"salary_max":158000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["computer-vision","healthcare","fine-tuning","generative-ai","cloud","robotics","reinforcement-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8263774","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T06:50:12Z","expires_at":"2026-11-13T17:03:28.756487Z","created_at":"2026-10-14T15:25:31.397968Z","updated_at":"2026-10-14T17:03:28.760236Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a91d5a21-3259-47d5-8df4-e146e013bc58"},{"id":"40768aec-20d6-40d2-8f34-07c3192e4d29","company_id":"a0000000-0000-0000-0000-000000000001","title":"Product Manager, Enterprise Privacy","slug":"product-manager-enterprise-privacy-3cd57f96","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role\n As a Product Manager focused on Enterprise Privacy, you'll collaborate with Privacy Engineering, Infrastructure, Trust and Safety, Legal, Security, GTM, enterprise and platform product teams, and our cloud partners to evolve and extend the controls that enterprises, regulated industries, and governments require in order to bring their most sensitive work to Claude. You will synthesize complex and fast-changing business needs and partner closely with the teams that retain, protect, and govern customer data across Anthropic's first-party platform and Claude on AWS, GCP, and Azure. Together, you will ensure that privacy guarantees are enforced by our systems and are verifiable by our customers.\n This is a high-visibility role with substantial scope. Anthropic offers layered controls over how customer data is retained and accessed, and you will build the product surface that lets customers understand, govern, and verify these controls. This includes retention and access controls, access logs and compliance reporting, customer-managed keys and custody, data residency, and self-serve user interfaces. Your stewardship of these privacy capabilities earns the customer trust that is essential to Anthropic's mission.\n Key responsibilities\n \n Deeply understand external customers with strict data handling requirements, from a bank's CISO to a healthcare system's compliance team to a government contractor's auditor, and the internal Trust and Safety, Legal, and GTM stakeholders whose needs pull in different directions.\n Partner with Privacy Engineering to ship controls that unblock regulated customers, balance impact against engineering cost, and deliver at Anthropic speed.\n Co-develop solutions with design-partner customers and their compliance teams, so shipped changes pass real audits, not just hypothetical ones.\n Work with engineers to steer architecture and investment decisions with a long-term lens: balancing build versus buy versus rely-on-the-cloud-provider, and sequencing custody, residency, and transparency across clouds that ship on their own timelines.\n Define success metrics for privacy controls, from preventing configuration incidents by design, to the speed at which customers can independently answer questions about their own data.\n Define and iterate on the self-serve experience for compliance and security teams, expanding what an organization can check about its own posture, settings, and access history without having to wait on support tickets.\n Gather and assess competing stakeholder needs and communicate prioritization trade-offs clearly to senior leadership, balancing privacy risk, safety requirements, legal exposure, and commercial impact. When something goes wrong, own what customers hear from us and when.\n Work with engineering leads to decide how retention, access, residency, and custody controls are enforced, so that our infrastructure keeps the promises we make.\n \n Minimum qualifications\n \n Experience taking a new product area from ambiguity to a durable roadmap, and shipping controls that enterprises or regulators depend on.\n A track record of driving platform capabilities that balance the needs of multiple customer segments and internal stakeholders.\n The judgment to make prioritization trade-offs between customer demand, risk, and engineering cost, and the clarity to communicate them.\n The ability to internalize how privacy, safety, legal risk, and commercial impact interact, and translate that understanding into a coherent product vision.\n Experience building highly collaborative and effective relationships with engineers and key cross-functional stakeholders. You have equal credibility providing design doc feedback to engineers, negotiating a control with a CISO without losing the relationship, and explaining a risk trade-off to leadership.\n \n Preferred qualifications\n \n 8+ years of product management experience, with deep exposure to privacy, security, or compliance products.\n Built or scaled audit logging, key management, data residency, retention, or access governance products for regulated customers.\n Shipped controls across multiple cloud providers or partnered with AWS, GCP, or Azure at the platform level.\n Worked with certification, attestation, or trust center programs (SOC 2, ISO 27001, FedRAMP).\n Used Claude, Claude Code, or comparable agentic tools and formed opinions about the data controls they should offer.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonu","salary_min":305000,"salary_max":385000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","alignment","agents","cloud"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5434369008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T17:23:27Z","expires_at":"2026-11-13T16:54:55.990746Z","created_at":"2026-10-14T15:17:22.412869Z","updated_at":"2026-10-14T16:54:55.994213Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/40768aec-20d6-40d2-8f34-07c3192e4d29"}],"page":1,"per_page":20,"total":1216,"total_is_exact":true,"total_pages":61}