{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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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 Developers and enterprises build on Claude through the Claude Platform. Our models are getting exponentially more intelligent, and our APIs and developer tools need to evolve just as quickly so developers can take advantage of that intelligence safely.\n We're hiring Engineering Managers to lead teams that build these APIs and developer tools. We are shipping APIs and infrastructure for building and running agents in production. These APIs ship alongside SDKs, docs, and tooling that developers (and agents!) utilize in order to build high quality integrations. These abstractions are backed by our identity, verification, and trust platform, which lets us scale access to Claude responsibly.\n Key responsibilities\n \n Lead a team of engineers to ship high quality work quickly, owning planning, prioritization, delivery, and operational health\n Partner with product to set your team's roadmap, and bring a strong point of view on what developers need\n Talk to customers directly and turn what you learn into engineering priorities\n Guide API design and architecture decisions for distributed systems that have to stay reliable as usage grows\n Work closely with Research, Safeguards, Security, and go-to-market teams to bring new model capabilities to developers safely\n Recruit, coach, and grow strong engineers\n \n Minimum qualifications\n \n You have experience managing high-performing software engineering teams shipping developer products, including hiring, coaching, and performance management\n You're product-minded and customer-oriented. You care about who uses what your team builds, and you've shaped roadmaps alongside product partners\n You've built and scaled distributed systems in production, and you have the technical depth required to guide architecture and API design\n You've led teams that ship quickly and hold a high quality bar\n You collaborate well across functions and can align teams you don't manage\n You care about Anthropic's mission and about developing AI safely\n \n Preferred qualifications\n \n Experience building agent infrastructure or products on top of LLMs\n Experience with identity, verification, access control, or trust and safety systems\n \n Deadline to apply:  None. Applications will be reviewed on a rolling basis. \n Location Preference: Preference will be given to candidates based in NY, SEA, SF or the Bay Area given the current location of team. \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 $405,000 — $485,000 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 candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before you","salary_min":405000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["api-design","distributed-systems","llm","alignment","platform"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5378643008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T04:15:36Z","expires_at":"2026-11-13T16:54:55.457822Z","created_at":"2026-10-14T15:17:21.754142Z","updated_at":"2026-10-14T16:54:55.461135Z","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/12e81e2e-7f84-4b3e-8fc0-0b66e2fcdb45"},{"id":"9cb8cda9-490d-4d6a-9dde-db4aec8490dd","company_id":"66e863fb-9aaf-40df-996c-eb439e6f857e","title":"Tech Lead Manager, AI Evaluations","slug":"tech-lead-manager-ai-evaluations-613503c9","description":"About Glean: \n  \n Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. \n  \n At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. \n  \n Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. \n  \n If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more. You'll be deeply embedded where people get things done. You'll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. \n About the Role: \n The bottleneck in building reliable and useful AI systems isn’t writing code anymore. It’s knowing what good looks like, applying that knowledge and experience from production usage into building rigorous evals, and using those evals to guide continuous product self-improvement loops. \n As the Tech Lead Manager of AI Evaluations, you will lead a team of applied AI scientists and engineers and build the evaluation strategy and platform that helps Glean ship delightful and reliable AI experiences. You will make evaluation a continuous part of how we develop and operate models, harnesses, assistants, agents, and other AI products at Glean. \n You’ll guide the team in translating emerging AI/ML research into reliable evaluation systems, build scalable, efficient and reusable eval infrastructure and trusted quality signals, and work across R\u0026D, product, and marketing teams to turn evaluation results into better products and customer outcomes. \n You will: \n \n Own the AI evaluation eng roadmap and technical direction end to end: from defining what success means and building the evaluation platform to integrating results into release and product decisions. \n Build scalable, reusable evaluation infrastructure for datasets, experiments, graders, trace capture and replay, result analysis, and dashboards. Make evaluations reproducible, versioned, cost-aware, and easy for product and engineering teams to use. \n Create and maintain representative evaluation sets from expert-curated examples, appropriately governed production signals, synthetic cases, and historical failures. Cover common workflows, edge cases, long-tail requests, adversarial inputs, and important user and domain slices; protect held-out acceptance sets from tuning leakage. \n Define a balanced quality measurement system for AI products, including correctness, groundedness, retrieval and context relevance, completeness, instruction following, citation quality, user preference, and task success. For agents, evaluate tool selection and arguments, multi-step trajectories, policy compliance, recovery, and final task state, not just the final response. \n Develop a fit-for-purpose mix of deterministic checks, model-based graders (including LLM-based autograders), and human evaluation. Establish clear rubrics; validate and calibrate automated judges against human judgments; and measure evaluator reliability and limitations so teams can trust and interpret scores. \n Integrate evaluation into development and operations: fast CI regression checks, broader pre-release suites, model and prompt comparisons, and sampled production evaluation. Build feedback loops from user feedback and production traces into new test cases, and use monitoring, canaries, and experiments to detect regressions and drift. \n Make quality tradeoffs measurable by connecting evaluation","salary_min":250000,"salary_max":350000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["cloud","llm","distributed-systems","alignment","agents","generative-ai","evaluation"],"apply_url":"https://job-boards.greenhouse.io/gleanwork/jobs/4742957005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T22:27:22Z","expires_at":"2026-11-13T16:55:35.432797Z","created_at":"2026-10-14T15:19:25.54086Z","updated_at":"2026-10-14T16:55:35.435547Z","company_name":"Glean","company_slug":"glean","company_logo_url":"https://www.google.com/s2/favicons?domain=glean.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9cb8cda9-490d-4d6a-9dde-db4aec8490dd"},{"id":"a403cbfc-5ab6-4009-833a-3a9a90e4f43c","company_id":"a0000000-0000-0000-0000-000000000001","title":"Data Engineer, Billing \u0026 Payments","slug":"data-engineer-billing-payments-d1c26c11","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 As a Data Engineer supporting Billing \u0026 Payments Engineering, you'll join the Finance Data Engineering team and build the foundation for accurate, scalable billing and revenue data across Anthropic. You'll work closely with Billing Engineering, Finance and Accounting, as well as Product and other partners, to turn billing and payments data into reliable metrics, reporting and insights. You'll make sure teams have trustworthy data that scales with our growth, and you'll lead your own projects to enable self-serve insights that help teams make data-driven decisions.\n Responsibilities: • Partner with Billing Engineering, Finance and Accounting to understand their data and reporting needs, and translate them into technical requirements • Design, build and maintain data models and pipelines in dbt that transform raw billing, usage and payments data into canonical datasets • Own revenue data accuracy and reconciliation, making sure billing, usage and revenue data tie out across systems • Define success metrics for billing systems, and build the data models and monitoring to track them • Set high data integrity standards and SLAs for timely, accurate delivery of billing and revenue data • Share on-call and data operations responsibilities for Finance Data Engineering pipelines • Build dashboards and self-serve data products that track core metrics and deliver insights across the company • Champion best practices for dbt, data modeling and SQL • Influence the Billing Engineering roadmap from a data systems perspective • Become an expert in our data models and data architecture\n You may be a good fit if you have: • 5+ years of experience as a Data Engineer, Analytics Engineer or in a similar role • Expertise in SQL and Python, and in building multi-step data pipelines with tools like dbt • Experience with workflow orchestration tools like Airflow, and version control with GitHub • Familiarity with billing concepts such as invoicing, payments, credits, refunds and subscriptions, and experience working with data from billing systems • Experience building dashboards and reporting in tools like Hex for multiple cross-functional teams • A bias for action and urgency, without letting perfect be the enemy of the effective • A full-stack mindset: you do what it takes to solve a problem end to end, even when it goes beyond the original scope • Comfort with ambiguity, and the initiative to create clarity and keep work moving • A passion for Anthropic's mission of building helpful, honest and harmless AI\n Strong candidates may also have: • Experience partnering with billing, finance or accounting teams on revenue metrics and reconciliation • Experience building an analytics or data engineering function from an early stage\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 $320,000 — $405,000 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 candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. T","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["payments","data-pipeline","alignment","data-engineering","data-science"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5450099008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T19:03:03Z","expires_at":"2026-11-13T16:54:55.362957Z","created_at":"2026-10-14T15:17:21.640971Z","updated_at":"2026-10-14T16:54:55.365861Z","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/a403cbfc-5ab6-4009-833a-3a9a90e4f43c"},{"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":"55139ac1-2db5-471c-b6aa-dbc4dc6745af","company_id":"a0000000-0000-0000-0000-000000000001","title":"Data Engineer, Product","slug":"data-engineer-product-96da0802","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 As a Data Engineer on the Data Science \u0026 Analytics team, you'll build the foundation that lets analytics scale across Anthropic. You'll partner with Engineering, Product and other teams to turn raw data into reliable metrics, reporting and insights, and you'll make sure teams have accurate metrics for our consumer products from idea to launch. You'll also lead your own projects that make self-serve insights possible, so teams can make data-driven decisions.\n Responsibilities \n \n \n Understand, and where possible anticipate, the data needs of partner teams, and translate them into data models, reporting and technical requirements\n \n Define, build and manage key dbt pipelines that turn raw logs into canonical datasets\n \n Set data integrity standards and SLAs so data is delivered on time and accurately\n \n Build reliable dashboards that track core metrics and share insights across the company\n \n Build foundational data products, dashboards and tools that let self-serve analytics scale\n \n Partner with stakeholders to define and materialize metrics and analysis for new and evolving consumer products\n \n Shape Product teams' roadmaps from a data systems perspective\n \n Become an expert in our data models and data architecture\n \n You may be a good fit if you have \n \n \n Significant experience as a Data Engineer or in a similar Data Science \u0026 Analytics role, ideally partnering with Product leads to build and report on company-wide metrics\n \n A passion for Anthropic's mission of building helpful, honest and harmless AI\n \n Expertise building multi-step ETL jobs with tools like dbt, plus experience with workflow tools like Airflow and version control through GitHub\n \n Expertise in SQL and Python for turning data into accurate, clean data models\n \n Experience building reporting and dashboards in tools like Hex that serve multiple cross-functional teams\n \n A bias for action, and a sense of when \"good enough\" beats perfect\n \n An end-to-end mindset: you take ownership of solving a problem fully, even when that means picking up work beyond your usual scope\n \n Comfort with ambiguity, and a habit of creating clarity and forward progress\n \n Experience using AI to scale your own productivity and your team's without lowering the quality of the work\n \n Strong candidates may also have \n \n \n Experience building a data engineering (or similar) function from the ground up in an early-stage or fast-growing environment\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 $320,000 — $405,000 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 candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, ","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","alignment","data-science","data-engineering"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5448481008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T17:00:40Z","expires_at":"2026-11-13T16:54:55.368278Z","created_at":"2026-10-14T15:17:21.646192Z","updated_at":"2026-10-14T16:54:55.371153Z","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/55139ac1-2db5-471c-b6aa-dbc4dc6745af"},{"id":"6146aded-8614-4fdf-a457-cef73b78ddbe","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Fullstack Software Engineer, Safeguards Engineering","slug":"staff-fullstack-software-engineer-safeguards-engineering-c0ee19d3","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 Note: this job post is for Fullstack Software Engineers only. If you are a Backend Engineer, please see our Backend Software Engineer post  here .  \n About the role \n We are looking for fullstack software engineers to help build safety and oversight mechanisms for our AI systems. As a software engineer on the Safeguards team, you will work to monitor models, prevent misuse, and ensure user well-being. This role will focus on building systems to detect unwanted model behaviors and prevent disallowed use of models. You will apply your technical skills to uphold our principles of safety, transparency, and oversight while enforcing our terms of service and acceptable use policies.\n We have multiple teams that are currently hiring within Safeguards. Team placement occurs after the interview process, taking into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented engineers where they'll have the greatest impact and growth potential.  \n Responsibilities: \n \n Design and build the internal review tools analysts use to investigate abuse — dashboards, case queues, and evidence viewers that make high-volume, high-stakes decisions fast and accurate.\n Build the interfaces where humans supervise agents: streaming output, interruption and approval flows, and clear provenance for every action an agent takes.\n Surface abuse patterns to our research teams through the visualizations and exploration tools that make emerging patterns legible.\n Own the frontend architecture for tools handling sensitive data at scale — performance under large datasets, correctness under real-time updates, and access controls enforced end to end.\n \n You may be a good fit if you have: \n \n A Bachelor’s degree in Computer Science, Software Engineering or comparable experience\n Proficiency in TypeScript and modern React; comfortable in Python for backend work\n The ability to work across the stack\n Strong communication skills and ability to explain complex technical concepts to non-technical stakeholders\n \n Strong candidates may also: \n \n 8+ years of experience in a software engineering position, not including internships or co-ops\n Have experience building data-dense internal tools or operator interfaces, not just marketing or consumer surfaces\n Have experience with integrity, spam, fraud, or abuse detection and mitigation\n Have experience building trust and safety detection mechanisms and intervention for AI/ML systems\n Have experience with prompt engineering, jailbreak attacks, and other adversarial inputs\n Have worked closely with operational teams to build custom internal tooling\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 $405,000 — $485,000 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 candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases,","salary_min":405000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","fine-tuning","rust","fullstack"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5369710008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T14:55:52Z","expires_at":"2026-11-13T16:54:56.598201Z","created_at":"2026-10-14T15:17:23.066476Z","updated_at":"2026-10-14T16:54:56.601343Z","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/6146aded-8614-4fdf-a457-cef73b78ddbe"},{"id":"84267438-bcb6-4a44-97ac-4f18bdb2ad77","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Software Engineer, Research Systems Engineering","slug":"staff-software-engineer-research-systems-engineering-27aecffe","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 Anthropic's Infrastructure organization builds and operates the distributed systems that train, serve, and secure our AI models. Every other team at Anthropic depends on these systems.\n In this role, you'll work directly with our Research teams to build the reliable, scalable, performant infrastructure our research depends on. This is a cross-functional systems team: you'll scope and lead complex, multi-month infrastructure projects and resolve the performance and scalability bottlenecks that limit how fast we can grow.\n Key responsibilities\n \n Independently scope and lead complex, multi-month infrastructure projects, from an ambiguous starting point through to a production system\n Build deep partnerships with researchers and Research teams to understand their needs and deliver for them\n Mentor other engineers and help raise the technical bar for the team\n Build alignment on technical direction across multiple teams, working through ambiguous problem spaces\n Take ownership of the reliability, scalability, and security of the systems you build as usage and complexity grow\n Lead the improvement of operational processes across Infrastructure, such as incident response, postmortems, and on-call rotations, so the team learns from every incident\n \n Minimum qualifications\n \n Experience designing, building, and operating large-scale distributed systems or infrastructure in production\n Experience independently scoping and delivering complex, ambiguous, multi-month technical projects\n Prior experience as a technical lead or mentor for other engineers\n Experience making architectural decisions that other engineers and teams build on top of\n Strong software engineering fundamentals and proficiency in at least one programming language (e.g., Python, Rust, Go, Java)\n Experience with modern cloud infrastructure (e.g., Kubernetes, infrastructure as code, AWS, GCP)\n Strong written and verbal communication skills, with experience building alignment across multiple teams or stakeholders\n \n Preferred qualifications\n \n 10+ years of software engineering experience, not including internships\n Experience with machine learning infrastructure (e.g., GPUs, TPUs, Trainium) and associated networking infrastructure (e.g., NCCL)\n Low-level systems experience (e.g., Linux kernel tuning, eBPF)\n Experience applying security or privacy engineering best practices\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 $320,000 — $485,000 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 candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're eve","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","alignment","cloud","research","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5447118008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T20:26:11Z","expires_at":"2026-11-13T16:54:56.973202Z","created_at":"2026-10-14T15:17:23.460298Z","updated_at":"2026-10-14T16:54:56.975982Z","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/84267438-bcb6-4a44-97ac-4f18bdb2ad77"},{"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"},{"id":"1fc06577-10ed-4c0e-9ff4-7109de64bb51","company_id":"a0000000-0000-0000-0000-000000000001","title":"Product Manager, Billing","slug":"product-manager-billing-36e0c8fe","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 in the Internal Products and Technologies organization, you'll partner with Engineering, Design, Growth, Trust \u0026 Safety, Support, and Finance to build the billing experiences every Claude customer uses, from the first purchase to the monthly invoice:\n \n We own how customers buy and manage Claude: choosing and changing plans, viewing invoices, tracking usage and spend, and setting spend controls.\n Your work reaches everyone who pays for Claude: individuals upgrading a plan, teams adding seats, developers buying credits, and enterprise admins managing budgets across thousands of users.\n You'll define how Claude should explain a charge, change a plan, or set a spend limit for a customer. You'll also define how admins set spend controls for the agents their teams run.\n You'll own billing experiences that convert well, resist abuse, and let customers resolve billing questions on their own.\n \n Responsibilities \n \n Deeply understand customers across segments (consumers, developers, teams, and enterprise admins) and where billing friction costs us conversion, trust, or support volume.\n Partner with Growth on purchase, upgrade, and plan-change flows, balancing conversion against clarity and long-term customer trust.\n Partner with Trust \u0026 Safety to stop abuse of Claude plans and credits without adding friction for legitimate customers.\n Design agentic billing experiences: Claude answering billing questions, resolving common issues, and taking billing actions, with a confirmation step wherever a mistake would cost the customer money.\n Partner with Engineering and Design so every Claude product gives customers the same clear way to buy, upgrade, and manage spend.\n Drive product strategy and roadmap, synthesize competing stakeholder needs, and communicate prioritization clearly to senior leadership.\n \n You may be a good fit if you: \n \n Have 7+ years of product management experience, including self-serve purchase, subscription, or billing experiences.\n Have shipped self-serve experiences where you owned conversion and retention metrics and ran experiments to move them.\n Have strong product design instincts, and can make a confusing concept like proration, overage, or credits clear on a single screen.\n Have built LLM-powered or agentic product features, and have a point of view on when an agent should act on its own and when it should ask first.\n Are equally credible discussing system design with engineers, abuse patterns with Trust \u0026 Safety, and funnels with Growth.\n Are comfortable making tradeoffs between growth, risk, and financial accuracy, and explaining them clearly.\n Are scrappy and resourceful, and do what it takes to get things done.\n \n Strong candidates may have: \n \n Shipped customer tools for usage-based or hybrid pricing (seats, usage, credits), such as usage dashboards, spend limits, and alerts.\n Worked with Trust \u0026 Safety or risk teams to prevent abuse of plans, credits, or free trials.\n Launched a product in new countries, including local pricing, tax, and currency.\n Built self-serve help experiences that measurably reduced billing support contacts.\n Scaled through hypergrowth (10x+ revenue).\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 $385,000 — $460,000 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 th","salary_min":385000,"salary_max":460000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["payments","agents","llm","alignment"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5434361008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T14:32:26Z","expires_at":"2026-11-13T16:54:55.967599Z","created_at":"2026-10-14T15:17:22.387227Z","updated_at":"2026-10-14T16:54:55.970345Z","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/1fc06577-10ed-4c0e-9ff4-7109de64bb51"},{"id":"4b085e70-3f1d-4d81-991d-d27552fd41a3","company_id":"a0000000-0000-0000-0000-000000000001","title":"Software Engineer, Staff: Applied AI, Science \u0026 Engineering","slug":"software-engineer-staff-applied-ai-science-engineering-b3110428","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 At Anthropic, we're building AI systems that are safe, beneficial, and transformative. Our mission is to develop AI that benefits humanity, and we believe the most powerful capabilities emerge when we thoughtfully bridge the gap between research breakthroughs and real-world applications.\n Claude is getting good at research. Given a well-posed problem, it can read the literature, write and run simulations, analyze results, and iterate on a design across physics, materials, chemistry, and engineering. But most of the hardest problems in science and engineering aren't well-posed. They live inside companies and labs with their own data, tools, constraints, and experts, and progress depends on turning all of that into work a model can actually do and check.\n Our Applied AI, Science \u0026 Engineering team takes Claude to the scientists and engineers working on hard problems in energy, materials, hardware design, and other physical-world fields. We build the tools  Claude needs to do real research and engineering work. Mostly that means software: the agent harnesses, integrations, evaluations, and working processes that turn a partner's problem into something Claude can make progress on and validate. We find real-world problems and test what we build through partner engagements, then feed our learnings back to our research and product teams. It's early, and the engineers who join now will shape how Claude gets applied to science and engineering.\n This is an engineering role first. You'll spend real time with domain experts, become the person who knows how to get Claude working in their field, and prototype quickly alongside them. Most of your time, though, goes into building and hardening the systems that make that work repeatable, not into writing recommendations or configuring someone else's product. A science or engineering background is a big plus, but what matters most is that you can earn the trust of expert researchers, turn a fuzzy problem into something concrete and checkable, and ship.\n Responsibilities \n \n Build the agent harnesses and research loops that let Claude carry a problem from literature review through hypothesis generation, simulation, analysis, and design iteration\n Connect scientific computing and simulation tools (finite element, CFD, electromagnetic, or molecular modeling codes, for example) so Claude can drive them reliably and at scale\n Design and build evaluations that tell us whether Claude's work is actually right, and be honest about where it isn't\n Sit with scientists and engineers at partner organizations to learn their problem, data, tools, and constraints, and turn open-ended questions into well-specified, verifiable tasks with clear success criteria\n Prototype with partners, ship pilots into their workflows, and cut anything that doesn't move the problem forward\n Turn what works in one engagement into shared tools, reusable components, and documented processes the next one can start from\n Be the technical voice on how Claude performs on science and engineering work, explaining results, limits, and tradeoffs clearly to researchers, engineering leads, and non-technical stakeholders alike\n Partner with our research and product teams to share where models fall short on science and engineering work, and help shape what comes next\n \n You may be a good fit if you \n \n Have 8+ years of experience building software, with strong engineering fundamentals and comfort across the stack, from data pipelines to tooling to quick interfaces\n Have built real systems with large language models, including agents, tool use, or evaluations\n Have worked on technical problems in a science or engineering field, such as physics, materials, chemistry, or electrical or mechanical engineering\n Have a track record of zero-to-one work in startup or startup-like environments\\\n Can work directly with expert users, understand their workflows deeply, and still keep the focus on building the right system rather than the one first requested\n Have good judgment about what can and can't be verified, and are comfortable saying a result isn't good enough yet\n Bring high agency, pick up new domains quickly, and hold strong opinions loosely\n Communicate clearly with researchers, engineers, and external partners, and care about the societal impacts of your work\n \n Strong candidates may also have \n \n An advanced degree or research experience in a physical science or engineering field\n Hands-on experience with scientific computing and simulation, numerical methods, or optimization\n Experience in industrial R\u0026D, a national lab, or a deep-tech company in areas like energy, mat","salary_min":405000,"salary_max":485000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","llm","alignment","agents"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5444766008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T04:04:18Z","expires_at":"2026-11-13T16:54:56.560265Z","created_at":"2026-10-14T15:17:23.031334Z","updated_at":"2026-10-14T16:54:56.562977Z","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/4b085e70-3f1d-4d81-991d-d27552fd41a3"},{"id":"2ac5e15c-2e6b-4cd6-97bf-677e8f0aae17","company_id":"a0000000-0000-0000-0000-000000000001","title":"AV Engineer, Platform \u0026 Automation","slug":"av-engineer-platform-automation-f6eeb6b0","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 an AV Engineer focused on Platform \u0026 Automation, you'll build and own the software layer of Anthropic's collaboration systems — the tools, integrations, and engineering practices that let a small AV team support a fast-growing global office portfolio. Our rooms, event spaces, and broadcast systems are increasingly managed through APIs and code: device management platforms, DSP and control systems, conferencing administration, ticketing, and paging. Your job is to turn that surface into reliable, team-owned tooling, and to bring software engineering discipline to how we build and maintain it.\n You can expect a lot of your time writing, shipping, and operating code — fleet health monitoring, automated room testing and ticketing, integrations between our AV platforms and IT systems, and the internal tools that AV Engineering, AV Operations, and Events rely on every day. You'll also help own the administration of the software platforms we already run, and set the standards for how the team version-controls, reviews, tests, releases, and maintains everything from DSP designs to firmware to internal services. \n Responsibilities \n \n Design, build, and ship internal tools and integrations that monitor, test, and manage AV and collaboration systems across every office\n Develop fleet health monitoring and automated room testing that catch failures before users report them and open actionable tickets automatically\n Build integrations across device and platform APIs — Q-SYS (including plugins and Lua control scripts), Logitech Sync, Google Meet hardware, digital signage; and IT systems such as Jira, Slack, PagerDuty, and Google Workspace\n Automate repetitive operational work: provisioning, configuration baselines, drift detection, certificate renewal, and asset data — with code others can read, test, and maintain\n Own administration and lifecycle of the software platforms behind the AV estate, including firmware and software release management with staged rollouts and rollback plans\n Operate internal tools as production services, with monitoring, alerting, service metrics, and runbooks, on team-owned repositories and service identities so nothing depends on one person\n Establish the team's engineering practices: source control, code review, testing, CI/CD, and release — and bring AV configuration under version control, with standards set for integrator deliverables and acceptance verified before changes land\n Enable AV Engineers, Operations Specialists, and Production staff to contribute safely through templates, guardrails, and review paths built for non-developers\n Partner with Security, IT Engineering, and Network on least-privilege API access, secrets management, and integration with enterprise standards\n Gather requirements from AV Operations, Events, and AV Engineering, prioritize the tooling roadmap by operational impact, and decide where to build, buy, or push a vendor to close a gap\n \n You May Be a Good Fit If You: \n \n Have 10+ years of experience across AV, collaboration, or IT systems engineering, including 4+ years writing, shipping, and maintaining production code that other people depend on\n Are fluent in at least one general-purpose language: Python, Lua, Go, TypeScript, or similar — and comfortable with shell scripting\n Practice engineering fundamentals by default: Git-based workflows, code review, automated testing, CI/CD, logging, and observability\n Understand how AV and collaboration systems work: DSP and control, conferencing platforms, room devices, signal flow, AV-over-IP — well enough to build reliable tools on top of them\n Have built integrations against REST APIs, webhooks, and vendor SDKs, and know how to handle auth, rate limits, pagination, retries, and partial failure\n Have owned a tool or service in production over time, not just written it, including fixing it when it broke and handing it off cleanly\n Have defined engineering practices for a team, especially one where most contributors aren't developers\n Turn operational pain into well-scoped tooling, and can say no to work that shouldn't be built\n Thrive in a fast-moving environment where standards are being written rather than inherited\n \n Strong Candidates May Also Have Experience With: \n \n Q-SYS Lua scripting and plugin development\n Logitech Sync and CollabOS, and Google Meet hardware administration through the Google Admin console and Admin SDK\n Google Workspace APIs, GCP (Cloud Run, Cloud Scheduler, Pub/Sub, Secret Manager), and Terraform or other infrastructure-as-code\n Jira, Slack, and PagerDuty APIs and automation\n Crestron, Extron, or other control system pr","salary_min":285000,"salary_max":325000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["cloud","llm","api-design","alignment","agents"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5383714008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-05T20:05:29Z","expires_at":"2026-11-13T16:54:55.237543Z","created_at":"2026-10-14T15:17:21.490869Z","updated_at":"2026-10-14T16:54:55.241018Z","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/2ac5e15c-2e6b-4cd6-97bf-677e8f0aae17"},{"id":"530711aa-611a-4a58-bb90-aff9ae5c0837","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff Research Engineer, Multi-Agent Scaling","slug":"staff-research-engineer-multi-agent-scaling-355bd081","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 Large teams of agents are starting to solve problems no single agent can, from rewriting major codebases to formalizing landmark mathematics. Our team studies how these teams scale: what happens to performance, cost and coordination as the number of agents, the compute budget and the length of the task grow, and what has to change to keep getting returns from that scale. We build the platform and evaluations Anthropic uses to run and measure large agent teams, and other research teams build on them.\n This role lives at the boundary between research and engineering. It is a generalist role on a small team: you'll design and run large experiments, build the systems they run on, and get to the bottom of surprising results. We often need to go from a vague question to a running experiment quickly.\n Responsibilities:\n \n Design, run and interpret large-scale experiments on agent teams, reasoning rigorously about what the data does and doesn't show\n Investigate how performance and efficiency change as team size, compute and task horizon grow, and find the bottlenecks that limit them\n Build and scale the systems that run very large agent teams reliably, and debug the failures that only appear at scale\n Design evaluations for long-horizon problems, and keep their results trustworthy\n Build the tooling and metrics that let researchers see what a large agent team is doing and why\n Partner with research teams across Anthropic so they can run their own experiments on the platform, and communicate findings clearly\n \n You may be a good fit if you:\n \n Have significant software engineering, ML or research engineering experience\n Have owned something substantial end to end, such as a large system, an evaluation or benchmark, an agent product, or a research project\n Genuinely enjoy both research and engineering work\n Think quantitatively about complex systems, and think twice before trusting a number\n Can work from a vague question rather than a spec\n Are results-oriented, with a bias towards flexibility and impact\n Have clear written and verbal communication\n Care about the societal impacts of your work\n \n Strong candidates may also have:\n \n Experience building or operating large-scale distributed systems, such as schedulers, sandboxed code execution, or inference and RL infrastructure\n Built evaluations, benchmarks or harnesses for LLMs or agents\n Experience building complex agentic systems that use LLMs\n Experience with scaling laws or other large-scale empirical research\n A background in operations research, statistics, economics, physics, quantitative finance, or another field that models and optimizes complex systems\n \n Strong candidates need not have:\n \n Formal certifications or education credentials\n Academic research experience or publication history\n Prior experience with multi-agent systems or reinforcement learning\n \n Representative projects:\n \n Measure how performance scales with the number of agents on a hard problem, and explain where the curve bends and why\n Prepare our largest-ever agent run: find what breaks as team size and task length grow together, and fix it before launch\n Work out how to allocate a fixed compute budget across a team of agents to solve a problem fastest\n Build tooling that turns the activity of a large agent team into something a researcher can read in minutes\n Design a novel eval that distinguishes real gains in teamwork from artifacts of the evaluation setup\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 $500,000 — $850,000 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 ","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","distributed-systems","search","agents","cloud","llm","alignment","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5442680008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-02T19:26:52Z","expires_at":"2026-11-13T16:54:56.618897Z","created_at":"2026-10-14T15:17:23.088259Z","updated_at":"2026-10-14T16:54:56.622222Z","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/530711aa-611a-4a58-bb90-aff9ae5c0837"},{"id":"a5d6d300-9651-452a-a441-c56d808d2290","company_id":"a0000000-0000-0000-0000-000000000001","title":"Software Engineer, Sandboxing","slug":"software-engineer-sandboxing-1cf5c2c9","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 Every time Claude runs code, uses a computer, or works as an agent, it does so inside a sandbox. Our Sandboxing team builds the isolation layer behind that work. The same systems run the code Claude writes for users, the environments we train and evaluate models in, and the agents our customers deploy.\n These workloads are untrusted by design, and there are a lot of them. Sandboxes need to start in milliseconds, hold up under adversarial code, and scale to a very large number of concurrent sessions. You will work at the boundary between the Linux kernel, virtualization, and the container ecosystem. You will partner with research, product, and security teams to make sure that safe and fast do not trade off against each other.\n Key responsibilities\n \n \n Design, build, and operate the sandboxing infrastructure that runs untrusted, model-generated code and agent workloads at scale\n \n Improve sandbox startup time, density, and throughput, including snapshot and restore, checkpointing, and warm pools\n \n Strengthen isolation across compute, filesystem, and network, including egress controls and per-session credential handling\n \n Investigate and fix performance, reliability, and security issues at the kernel, hypervisor, and container runtime level\n \n Build the monitoring, tracing, and debugging tools that show where time and resources go inside a sandbox\n \n Work with researchers to support reinforcement learning environments and evaluations that need fast, reproducible, isolated execution\n \n Work with product and platform engineers to expose sandboxes as a dependable, well-designed capability for Claude products and customers\n \n Partner with security teams to threat-model the platform and to test, harden, and respond to escape and abuse scenarios\n \n Work with cloud providers on the hardware and platform features that our workloads depend on\n \n Share knowledge on systems programming and Linux internals, and help set technical standards for the team\n \n Minimum qualifications\n \n \n Software engineering experience in Linux systems programming, kernel development, or another low-level area\n \n Strong programming skills in at least one systems language such as Rust, C, C++, or Go\n \n Working knowledge of virtualization or container isolation technologies such as KVM, QEMU, Firecracker, gVisor, or runc\n \n Understanding of Linux resource management, including namespaces, cgroups, scheduling, and memory management\n \n Experience profiling and debugging performance issues at the system level\n \n Ability to work in unfamiliar codebases and technical areas, and to ship practical solutions with measurable results\n \n Strong communication skills and a commitment to building safe, reliable systems\n \n Preferred qualifications\n \n \n Experience building or operating a multi-tenant sandbox, serverless, or code execution platform\n \n Experience with microVMs, user-space kernels, or lightweight virtualization, including snapshot and restore\n \n Experience with OS-level security hardening, including seccomp, LSMs, and threat modeling of untrusted code\n \n Experience with eBPF and kernel tracing tools\n \n Experience with Kubernetes and cloud infrastructure at scale (AWS, GCP)\n \n Experience with network isolation, high-performance networking, or building egress proxies and policy layers\n \n Experience with sandboxes for AI agents, computer use, or reinforcement learning environments\n \n Experience with GPU virtualization, accelerators, or performance work on machine learning workloads\n \n Prior experience as a technical lead or mentor\n \n Representative projects\n \n \n Cutting sandbox cold start from seconds to milliseconds with snapshot-based restore and pre-warmed pools\n \n Designing a network egress policy layer so agents can reach approved services without exposing secrets\n \n Running many thousands of parallel reinforcement learning environments with strong isolation between them\n \n Building tracing tools that attribute latency and resource use inside a running sandbox\n \n Reducing per-sandbox memory overhead to raise density on each host\n \n Hardening the runtime against escape attempts found in internal red-team exercises\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 $320,000 — $485,000 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","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","alignment","agents","cloud","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5440427008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-01T15:27:13Z","expires_at":"2026-11-13T16:54:56.554954Z","created_at":"2026-10-14T15:17:23.025457Z","updated_at":"2026-10-14T16:54:56.558118Z","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/a5d6d300-9651-452a-a441-c56d808d2290"},{"id":"5e7d165a-b53c-4ed3-9498-73273b6b1e83","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer / Research Scientist, RL Frontiers","slug":"research-engineer-research-scientist-rl-frontiers-f4d3a293","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 Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale.\n This role sits squarely across research and engineering. You'll develop next-generation architectures and RL algorithms, take them from a small-scale result to a frontier-scale run, and understand every place they behave differently along the way. You'll build the systems that set how fast the team can iterate: how many experiments, at what scale, and how quickly we can trust the results. And you'll work on Anthropic's largest and fastest RL runs, where the gap between a good idea and a working one is often a problem no one has solved yet.\n Key responsibilities\n \n Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient\n Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale\n Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic\n Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale\n Own end-to-end performance of our largest RL runs, from research code down to the hardware\n Build performance and cost models for proposed architecture and algorithm changes, and use them to decide which ideas get scaled\n Investigate training dynamics at scale, including instabilities, divergence, and throughput regressions, and trace them to root cause\n \n Minimum qualifications\n \n Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization\n Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism\n A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or production impact\n Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute\n Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm\n Strong programming skills in Python and JAX or PyTorch, and comfort reading and changing code at every layer of the stack\n \n Preferred qualifications\n \n Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise\n Experience developing RL algorithms for language models\n Experience with scaling laws or other quantitative models of training efficiency\n Experience designing or modifying transformer architectures beyond standard configurations\n Experience scaling training to large fleets of accelerators and debugging the problems that only appear at scale\n Deep understanding of numerics in large-scale training, including low-precision formats and sources of instability\n Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices\n Experience with C++ or Rust\n \n Representative projects\n \n Characterize how a new RL algorithm's throughput and learning efficiency change from small models to frontier scale, and fix what breaks\n Develop a new attention variant, get it working at full scale, and measure how its quality and throughput compare to the baseline\n Prepare our next largest-ever RL run: find what breaks when model size, context length, and compute all grow at once, and fix it before launch\n Trace a loss instability that only appears past a certain scale to its root cause, and work out whether the fix belongs in the algorithm, the numerics, or the system\n Build a model that predicts the throughput and cost of a proposed architecture change before anyone writes the kernel\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 $500,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["alignment","jax","reinforcement-learning","cloud","pytorch","search","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5438044008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T19:03:37Z","expires_at":"2026-11-13T16:54:56.207892Z","created_at":"2026-10-14T15:17:22.661233Z","updated_at":"2026-10-14T16:54:56.210539Z","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/5e7d165a-b53c-4ed3-9498-73273b6b1e83"},{"id":"f52f56d2-71f3-41b7-97a4-f3faf0cfdfb8","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer / Performance Engineer, RL Distributed Systems","slug":"research-engineer-performance-engineer-rl-distributed-systems-28be6fc4","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 Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is an unusually demanding distributed system. Training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts, exchange data continuously, and have to keep making progress while hardware fails, load shifts, and the research changes underneath them. How well that system holds together determines how much of our compute turns into learning, and how quickly the team can try the next idea.\n As a Research Engineer on the Distributed Systems team within RL Engineering, you'll work on whatever part of that system is the current limit. That might be scheduling and placement, data movement between components, running large numbers of sandboxed environments, storage and checkpointing, networking, fault tolerance, autoscaling, or the observability that tells us what a run is actually doing. We're looking for generalists: engineers who can move between these layers, reason from first principles about a system they haven't seen before, and pick the problem that matters most rather than the one closest to their prior experience.\n Our system changes as fast as the research does, correctness under failure matters as much as throughput, and the best solutions often come from understanding the ML workload well enough to know which guarantees it actually needs. Strong candidates have built and run large distributed systems, care about getting the details right, and want to apply that experience to a workload that is very large, very heterogeneous, and changing quickly.\n Key responsibilities\n \n Design, build, and operate the distributed systems that run RL at scale, across training, sampling, and environment execution\n Find and remove whatever currently limits the system, whether it's scheduling, data movement, storage, networking, or coordination\n Build fault tolerance into every layer: failure detection, isolation, and recovery that keep long-running jobs making progress without human intervention\n Design resource management and autoscaling so that compute follows demand as a run's needs shift\n Build observability that makes it possible to understand what a run is doing and why it slowed down, stalled, or produced unexpected results\n Build automation that detects and remediates common problems, and design interfaces that let engineers and automated tools operate runs safely\n Work with researchers and performance engineers to make sure systems changes preserve training correctness and don't introduce subtle nondeterminism\n Remove classes of failure at their source through incident review, testing, and redesign, and write clear design documents for what you build\n \n Minimum qualifications\n \n Strong software engineering skills in Python and at least one systems language such as Rust, C++, or Go\n Experience designing, building, and operating large-scale distributed systems in production\n Deep understanding of distributed systems fundamentals, including consistency, coordination, consensus, failure modes, and recovery\n Ability to reason quantitatively about throughput, latency, and resource costs across compute, memory, storage, and network\n Experience debugging complex failures across many hosts and services, including failures you can't reproduce locally\n Strong written communication, including design documents and incident writeups\n \n Preferred qualifications\n \n Experience running ML training or inference infrastructure at scale\n Experience across several layers of the stack, such as scheduling, storage, networking, and orchestration\n Experience building schedulers, autoscalers, or resource management systems\n Experience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scale\n Experience with high-performance networking, RDMA, or collective communication libraries\n Experience building observability or automated remediation for large fleets\n Experience with async Python frameworks such as Trio or asyncio\n Familiarity with reinforcement learning or large language model training workloads\n \n Representative projects\n \n Design a scheduler that places training, sampling, and environment work across a heterogeneous cluster while respecting network topology and failure domains\n Build a failure detection and recovery system that lets a long-running job survive host and network failures with minimal lost work\n Scale environment execution substantially without increasing tail latency for the training step\n Design an autoscaling policy that rebalances compu","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","llm","search","alignment","distributed-systems","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5438030008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T19:03:34Z","expires_at":"2026-11-13T16:54:56.168786Z","created_at":"2026-10-14T15:17:22.612611Z","updated_at":"2026-10-14T16:54:56.171453Z","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/f52f56d2-71f3-41b7-97a4-f3faf0cfdfb8"},{"id":"80815516-8760-4259-84ea-0a29c4be14bd","company_id":"a0000000-0000-0000-0000-000000000001","title":"Applied AI Engineer, Beneficial Deployments (Life Sciences)","slug":"applied-ai-engineer-beneficial-deployments-life-sciences-60334277","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 Beneficial Deployments: \n Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, foundations, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences — focusing on raising the floor for those who need it most.\n About the Role: \n We're looking for an Applied AI Engineer to join our Beneficial Deployments team, focused on maximizing the impact of Claude in the life sciences. Our goal is ambitious: accelerate scientific progress from R\u0026D through translation by an order of magnitude. That means making Claude the go-to tool for the life sciences ecosystem from early discovery in academia to paradigm shifting biotech to reimaging pharma pipelines  — and building the technical infrastructure to back that up.\n You'll work directly with flagship research partners like Howard Hughes Medical Institute and The Allen Institute, embedded in their scientific workflows. This isn't consulting from the outside — you'll be building alongside their engineers, prototyping agents that fit into real research pipelines, and developing the ecosystem-level tooling (MCP servers, benchmarks, reusable agent skills) that extends Claude's usefulness across the broader life sciences community. This role will be part of the founding Beneficial Deployments applied AI team focused on bringing more of life sciences closer to the frontier and be responsible for building with our partners. \n Responsibilities: \n \n \n Partner deeply with flagship life sciences research institutions — understand their scientific workflows end-to-end, build hands-on with their engineering teams, and help take projects from early exploration to production systems integrated into how they do science day-to-day.\n \n Develop reusable ecosystem infrastructure, like MCP servers for domain-specific data sources (genomics platforms, literature databases, experimental repositories), instruments, scientifically-grounded benchmarks, and agent skills that other institutions can adopt without starting from scratch.\n \n Identify what's actually hard about deploying AI in life sciences (heterogeneous data, auditability requirements, the prototype-to-trust gap) and feed those findings back to product, engineering, and research.\n \n Create technical content and documentation that lets partners self-serve, so what works for one institution can scale globally without the same level of hand-holding.\n \n You Might Be a Good Fit If You Have: \n \n \n 4+ years as a Software Engineer, Forward Deployed Engineer, or technical founder — with production experience shipping systems that real users depend on.\n \n Deep research experience in life sciences, biomedical research, or scientific computing. Bonus if you've studied genomics, neuroscience, or drug discovery specifically and are comfortable getting deeply technical with academics.\n \n Experience building LLM-powered tools or applications: prompting, context engineering, agent architectures, evaluation frameworks.\n \n Builder credibility from shipping production code as a software engineer, forward-deployed engineer, or technical founder.\n \n A scrappy mentality–comfortable wearing multiple hats, building from scratch, driving clarity in ambiguous situations, and doing whatever it takes to further the mission.\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 $280,000 — $320,000 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 quali","salary_min":280000,"salary_max":320000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","llm","healthcare","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5437172008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T02:57:03Z","expires_at":"2026-11-13T16:54:55.178807Z","created_at":"2026-10-14T15:17:21.424593Z","updated_at":"2026-10-14T16:54:55.181589Z","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/80815516-8760-4259-84ea-0a29c4be14bd"},{"id":"c3e4ef9c-03db-471a-a384-685b963ad567","company_id":"a0000000-0000-0000-0000-000000000001","title":"Senior+ Software Engineer, Legal Tech","slug":"senior-software-engineer-legal-tech-2d4cba53","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 Anthropic's Legal Tech engineers play a specialized and critical role in keeping Claude at the frontier. Partnering closely with counsel, we provide custom infrastructure and guidance for complex, novel technical matters, empowering teams across the company to continue meeting our legal obligations as we innovate.\n As a Senior+ Software Engineer on our Legal Tech team, you'll build dedicated, highly secure, and reliable cloud environments and data processing and storage capabilities at speed and scale. You will own your scope end to end: from raw requirements all the way through to large-scale production systems that satisfy legal invariants and unlock new possibilities. The systems you build won't just be dependable and scalable; they'll be interpretable and auditable, demonstrating to skeptical audiences exactly how they worked and why results can be trusted.\n You'll be an essential member of a collaborative team, working together to solve difficult problems against fixed deadlines. You will own internal customer relationships as well: you'll gather, prioritize, and coordinate needs from colleagues on Privacy, Security, Research, and/or Infra as well as Legal, to ship the right capabilities on the right timelines. You'll act as a trusted advisor: clearly explaining technical concepts and constraints to inform legal decisions, and proactively designing new approaches to prevent classes of problems. Your infrastructure expertise will directly empower our business to calibrate and manage risk, innovating faster thanks to systems that build in legal considerations.\n Key responsibilities\n \n \n Build and operate dedicated cloud infrastructure, storage, and data pipelines for specialized workloads that can't run on general-purpose systems\n \n Design architectures with excellent security, privacy, reliability, scalability, and auditability baked in\n \n Partner with a wide range of internal customer teams to translate requirements into sound approaches and realistic ETAs\n \n Deliver against fixed deadlines\n \n Support complex and novel legal requests on controlled infrastructure, with verifiable chain of custody\n \n Pair with engineering teams across the company to extend existing infrastructure to support new workflows and/or legal requests\n \n Work with nontechnical stakeholders to clarify their needs through responsive, easy-to-follow dialogue\n \n Minimum qualifications\n \n \n Experience designing, building, and operating highly trusted infrastructure and data systems in production, including owning architectural decisions\n \n Experience working across large, complex, multi-team environments, including in stacks and code you don't own\n \n Ability to take ownership of a cloud account and secure it end to end (networking, storage, identity, encryption, least-privilege provisioning, etc.) on AWS and/or GCP\n \n Ability to translate legal and privacy requirements into sound technical designs and implementations that teams are excited to adopt\n \n A habit of verifying rather than assuming: designing checks for invariants and plainly naming what isn't proved\n \n Self-directed in a high-change environment, and comfortable resequencing quickly when new needs emerge\n \n Clear communication with nontechnical stakeholders, and an understanding of their needs\n \n Preferred qualifications\n \n \n Software engineering experience with an infrastructure, data, privacy, and/or security focus\n \n Proficiency with large-scale data tooling, such as distributed processing, lakehouse table formats, and/or query engines\n \n Understanding of data processing pipelines, LLM training, and/or provenance and lineage\n \n Experience with secure enclaves, confidential computing, clean rooms, or other purpose-built environments for sensitive data\n \n Background in eDiscovery, litigation support, or records retention at scale, including chain of custody and verifiable integrity\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 $320,000 — $485,000 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 ","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["alignment","llm","cloud","data-pipeline","legal","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5435665008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T01:56:42Z","expires_at":"2026-11-13T16:54:56.468394Z","created_at":"2026-10-14T15:17:22.938503Z","updated_at":"2026-10-14T16:54:56.472145Z","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/c3e4ef9c-03db-471a-a384-685b963ad567"},{"id":"355cddc4-1109-480c-9753-ab804302195a","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Software Engineer, Account Abuse (Machine Learning)","slug":"staff-software-engineer-account-abuse-machine-learning-ae303349","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 The Account Abuse team is tasked with ensuring Anthropic's computing capacity is allocated fairly, minimizing resources available to bad actors and preventing them from coming back. As a software engineer on this team, you will build the machine learning systems that help us detect and stop abuse at scale. The ideal candidate can see things from opponents' perspectives, understand their means and motives, and anticipate their responses to countermeasures.\n We're looking for full stack machine learning engineers with experience across model training, productionization, and evaluation. You'll also look for ways to use Claude to speed up how these models get built and maintained.\n This is classical ML on structured and behavioral data. You do not need a deep learning background or knowledge of LLM internals. What matters is that you have trained and shipped models where the stakes are real, and that you care about building robust production systems as much as the model itself. A false positive here is a legitimate customer locked out, so measurement, precision, and safe rollout are part of the job.\n Key responsibilities \n \n Build and operate a feature computation platform that serves both model training and real-time scoring, with point-in-time correct training data and low-latency online retrieval\n Train, evaluate, and deploy models that detect account-level abuse and fraud, running them both offline and online\n Build tooling that automates more of the model development lifecycle, including using Claude to speed up feature development, training, and evaluation\n Make backtesting, shadow deployment, and staged rollout the default path to production, with monitoring for training / serving skew, drift, and adversarial adaptation\n Work with our data scientists and our Policy \u0026 Enforcement team to improve label coverage and quality\n Partner with product and platform teams to gather signals and integrate model decisions with minimal impact on their systems' latency, stability, or overall architecture\n \n Minimum qualifications \n \n Proficiency in Python and SQL\n Experience training machine learning models and deploying them to production\n Experience building data pipelines with a batch processing engine (e.g., Spark, Beam) and a workflow scheduler (e.g., Airflow)\n Working understanding of point-in-time correctness and training / serving skew, and how to prevent both\n Strong communication skills and ability to explain technical tradeoffs to non-technical stakeholders\n \n Preferred qualifications \n \n Experience building or operating a feature platform such as Chronon, Feast, or Tecton\n Experience with stream processing engines such as Flink, Beam / Dataflow, or Kafka Streams\n Experience training ML models in a production setting with demanding serving requirements, such as fraud, risk, or ranking\n Experience with tree-based models on tabular data\n Experience building unsupervised, clustering-based or graph-based detection systems to surface coordinated account abuse\n Experience in integrity, spam, fraud, or abuse detection\n Experience working with scarce, delayed, or noisy labels\n Experience with AutoML or other approaches to automating the ML workflow\n Care about the societal impacts of AI and want your work to make powerful systems safer\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 $320,000 — $485,000 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 b","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","alignment","data-pipeline","deep-learning","machine-learning","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5436293008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-28T20:31:13Z","expires_at":"2026-11-13T16:54:56.684975Z","created_at":"2026-10-14T15:17:23.151497Z","updated_at":"2026-10-14T16:54:56.687848Z","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/355cddc4-1109-480c-9753-ab804302195a"},{"id":"901acfd2-6d66-4082-b7e6-b0ba1754c580","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff + Sr. Software Engineer, Cloud Inference","slug":"staff-sr-software-engineer-cloud-inference-b11fdf49","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 The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.\n Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic's most precious resources: compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need product-minded backend engineers who can navigate these platform differences, design the services and abstractions that work across providers, and make architectural decisions that keep us reliable and cost-effective at massive scale.\n Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards.\n Key responsibilities \n \n Design, build, and own backend services and infrastructure that serve Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models\n Work cross-functionally with internal inference, product API, systems, and security teams, among others, and with CSP partners to stand up the full serving stack on new cloud platforms, resolve operational issues, and influence provider roadmaps\n Build and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions\n Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management, scale across providers, and reduce per-platform complexity\n Contribute to capacity planning, autoscaling, and workload routing strategies that match supply with demand and direct requests to the most cost-effective accelerator and region\n Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads\n \n Minimum qualifications \n \n Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users\n Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration\n Are curious about LLM serving; prior inference or ML experience is not required\n Thrive in cross-functional collaboration with both internal teams and external partners\n Have experience working with external partners to align goals and deliver impact\n Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems\n Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description\n \n Preferred qualifications \n \n Direct experience working with CSPs to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings\n Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments\n Solid understanding of multi-region deployments, geographic routing, and global traffic management\n Proficiency in Python or Rust\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 $320,000 — $485,000 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 Vis","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","payments","llm","alignment","infrastructure","inference"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5436684008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-28T18:53:28Z","expires_at":"2026-11-13T16:54:57.029878Z","created_at":"2026-10-14T15:17:23.514386Z","updated_at":"2026-10-14T16:54:57.033374Z","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/901acfd2-6d66-4082-b7e6-b0ba1754c580"},{"id":"c8280610-a672-4bc7-9693-7e8cdab09b06","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff + Sr. Software Engineer, Cloud Inference Launch Engineering","slug":"staff-sr-software-engineer-cloud-inference-launch-engineering-435acf86","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 The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.\n Within Cloud Inference, the model \u0026 inference launch team owns the validation pipeline for our inference server and load balancer on these platforms. We're responsible for every inference change — model launches, performance improvements, safeguard integrations — landing on cloud platforms with correctness, performance, and reliability intact.\n This is high-leverage infrastructure work: validation has to be fast and cheap enough to run on the same accelerators that serve customers, trustworthy enough to replace manual checks, and consistent enough that a change working on Anthropic first-party means it works everywhere. This directly determines how fast frontier models and features ship to every cloud platform, and how quickly performance wins reach production — reclaiming capacity at a time when compute is our scarcest resource.\n Key responsibilities\n \n Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first-party platform\n Work with the core inference team to bring new inference features (e.g. structured sampling, prompt caching, and more) to cloud platforms, owning the platform-specific integration that gets them to production\n Identify and dive deep on the gaps that make inference behave differently across first-party and CSPs — config drift, observability, deployment patterns, hard cross-platform bugs — and fix them at the source rather than building platform-specific workarounds\n Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines (throughput and latency), and correctness checks that catch regressions before production\n Drive down merge-to-production cycle time by making validation faster, more parallel, and cost-effective enough to run on the same constrained accelerator pool that serves customers, without trading away reliability \n Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads\n \n Minimum qualifications\n \n Have a strong interest in LLM serving; prior inference or ML experience is not required \n Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users\n Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability\n Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration\n Thrive in cross-functional collaboration with both internal teams and external partners\n Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems\n Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description\n \n Preferred qualifications\n \n LLM inference optimization, batching, and caching strategies\n Capacity-constrained scheduling or shared-resource test infrastructure\n Solid understanding of multi-region deployments, request routing, load balancing, global traffic management\n Working with CSP partner teams to scale infrastructure across multiple platforms, navigating differences in networking, security, privacy, and managed service\n Proficiency in Python or Rust\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 $320,000 — $485,000 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 hybri","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","llm","distributed-systems","inference","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5436697008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-28T18:52:20Z","expires_at":"2026-11-13T16:54:57.035074Z","created_at":"2026-10-14T15:17:23.523759Z","updated_at":"2026-10-14T16:54:57.037886Z","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/c8280610-a672-4bc7-9693-7e8cdab09b06"}],"page":1,"per_page":20,"total":478,"total_is_exact":true,"total_pages":24}