{"schemaVersion":"jobsearcher.job.v1","id":"1460d19351c5f81ab23bd452","url":"https://jobsearcher.com/jobs/1460d19351c5f81ab23bd452","canonicalUrl":"https://jobsearcher.com/jobs/1460d19351c5f81ab23bd452","title":"Anthropic Fellows Program Reinforcement Learning","description":"About Anthropic\nAnthropic’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\nAnthropic Fellows Program overview\nThe Anthropic Fellows Program is designed to foster AI research and engineering talent. We provide funding and mentorship to promising technical talent – regardless of previous experience.\n\nFellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In one of our earlier cohorts, over 80% of fellows produced papers.\n\nWe run multiple cohorts of Fellows each year and review applications on a rolling basis. This application is for cohorts starting in July 2026 and beyond.\n\nWhat to expect\n\n4 months of full‑time research\n\nDirect mentorship from Anthropic researchers\n\nAccess to a shared workspace (in either Berkeley, California or London, United Kingdom)\n\nConnection to the broader AI safety and security research community\n\nWeekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (varies by country)\n\nFunding for compute (≈15k USD per month) and other research expenses\n\nInterview process\nThe interview process will include an initial application and reference check, technical assessments and interviews, and a research discussion.\n\nWe encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification listed. Research shows that people who identify as being from underrepresented groups are more prone to 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.\n\nCompensation\nThe expected base stipend for this role is 3,850 USD / 2,310 GBP / 4,300 CAD per week, with an expectation of 40 hours per week for 4 months (possible extension).\n\nFellows workstreams\nDue to the success of the Anthropic Fellows for AI Safety Research program, we are expanding it across teams at Anthropic. We expect a significant overlap in the types of skills and responsibilities across the roles and will by default consider candidates for all the workstreams.\n\nAI Safety Fellows\n\nAI Security Fellows\n\nML Systems & Performance Fellows\n\nReinforcement Learning Fellows\n\nEconomics & Societal Impacts Fellows\n\nAcross the workstreams, you may be a good fit if you:\n\nAre motivated by making sure AI is safe and beneficial for society as a whole\n\nAre excited to transition into empirical AI research and would be interested in a full‑time role at Anthropic\n\nHave a strong technical background in computer science, mathematics, or physics\n\nThrive in fast‑paced, collaborative environments\n\nCan implement ideas quickly and communicate clearly\n\nStrong candidates may also have:\n\nStrong background in a discipline relevant to a specific Fellows workstream (e.g. economics, social sciences, or cybersecurity)\n\nExperience in areas of research or engineering related to their workstream\n\nCandidates must be:\n\nFluent in Python programming\n\nAvailable to work full‑time on the Fellows program\n\nReinforcement Learning Fellows\nFellows will undergo a project selection and mentor matching process. Potential research areas and mentors include:\n\nRuhua Jiang\n\nKaidi Cao\n\nSunny Duan\n\nDavid Brandfonbrener\n\nColt Steele\n\nDino Distefano\n\nWill Williams\n\nProjects in this workstream may include:\n\nBuilding model‑based tools to better understand AI training data and improve training data quality\n\nA research project to better understand generalization\n\nCreating RL environments to improve Claude models within your domain of expertise\n\nBuilding RL environments for safety‑related tasks\n\nConducting research and implementing solutions in areas such as RL algorithms\n\nUnique candidate criteria\nYou might be a particularly great fit for this workstream if you:\n\nHave strong software engineering skills with experience building complex ML systems\n\nCan balance research exploration with engineering rigor and operational reliability\n\nEnjoy collaborating across research and engineering disciplines\n\nAre comfortable working with large‑scale distributed systems and high‑performance computing\n\nHave experience with training, fine‑tuning, or evaluating large language models\n\nAre adept at analyzing and debugging model training processes\n\nLogistics\nTo participate in the Fellows program, you must have work authorization in the United States, United Kingdom, or Canada and be located in that country during the program.\n\nWe have designated shared workspaces in London and Berkeley where fellows will work from and mentors will visit. We are also open to remote fellows in the United Kingdom, United States, or Canada. We will ask you about your availability to work from Berkeley or London (full‑ or part‑time) during the program.\n\nWe are not currently able to sponsor visas for fellows. To participate, you need to have or independently obtain full‑time work authorization in the United Kingdom, United States, or Canada.\n\nThe program runs for 4 months, full‑time. If you can’t commit to the full duration, please still apply and note your constraints in the application. We review these requests on a case‑by‑case basis.\n\nPlease note: We do not guarantee that we will make any full‑time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit for full‑time roles at Anthropic. In previous cohorts, 25–50% of fellows received a full‑time offer, and we’ve supported many more to go on to do great work on AI safety and security at other organizations.\n\nWe encourage diverse applicants\nWe encourage you to apply even if you do not believe you meet every single qualification. We value representation and strive to include a range of diverse perspectives on our team.\n\n#J-18808-Ljbffr","company":"NerdLevel Tech","rawCompany":"nerdleveltech","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-06-20T04:44:10.892Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Anthropic Fellows Program Reinforcement Learning","description":"About Anthropic\nAnthropic’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\nAnthropic Fellows Program overview\nThe Anthropic Fellows Program is designed to foster AI research and engineering talent. We provide funding and mentorship to promising technical talent – regardless of previous experience.\n\nFellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In one of our earlier cohorts, over 80% of fellows produced papers.\n\nWe run multiple cohorts of Fellows each year and review applications on a rolling basis. This application is for cohorts starting in July 2026 and beyond.\n\nWhat to expect\n\n4 months of full‑time research\n\nDirect mentorship from Anthropic researchers\n\nAccess to a shared workspace (in either Berkeley, California or London, United Kingdom)\n\nConnection to the broader AI safety and security research community\n\nWeekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (varies by country)\n\nFunding for compute (≈15k USD per month) and other research expenses\n\nInterview process\nThe interview process will include an initial application and reference check, technical assessments and interviews, and a research discussion.\n\nWe encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification listed. Research shows that people who identify as being from underrepresented groups are more prone to 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.\n\nCompensation\nThe expected base stipend for this role is 3,850 USD / 2,310 GBP / 4,300 CAD per week, with an expectation of 40 hours per week for 4 months (possible extension).\n\nFellows workstreams\nDue to the success of the Anthropic Fellows for AI Safety Research program, we are expanding it across teams at Anthropic. We expect a significant overlap in the types of skills and responsibilities across the roles and will by default consider candidates for all the workstreams.\n\nAI Safety Fellows\n\nAI Security Fellows\n\nML Systems & Performance Fellows\n\nReinforcement Learning Fellows\n\nEconomics & Societal Impacts Fellows\n\nAcross the workstreams, you may be a good fit if you:\n\nAre motivated by making sure AI is safe and beneficial for society as a whole\n\nAre excited to transition into empirical AI research and would be interested in a full‑time role at Anthropic\n\nHave a strong technical background in computer science, mathematics, or physics\n\nThrive in fast‑paced, collaborative environments\n\nCan implement ideas quickly and communicate clearly\n\nStrong candidates may also have:\n\nStrong background in a discipline relevant to a specific Fellows workstream (e.g. economics, social sciences, or cybersecurity)\n\nExperience in areas of research or engineering related to their workstream\n\nCandidates must be:\n\nFluent in Python programming\n\nAvailable to work full‑time on the Fellows program\n\nReinforcement Learning Fellows\nFellows will undergo a project selection and mentor matching process. Potential research areas and mentors include:\n\nRuhua Jiang\n\nKaidi Cao\n\nSunny Duan\n\nDavid Brandfonbrener\n\nColt Steele\n\nDino Distefano\n\nWill Williams\n\nProjects in this workstream may include:\n\nBuilding model‑based tools to better understand AI training data and improve training data quality\n\nA research project to better understand generalization\n\nCreating RL environments to improve Claude models within your domain of expertise\n\nBuilding RL environments for safety‑related tasks\n\nConducting research and implementing solutions in areas such as RL algorithms\n\nUnique candidate criteria\nYou might be a particularly great fit for this workstream if you:\n\nHave strong software engineering skills with experience building complex ML systems\n\nCan balance research exploration with engineering rigor and operational reliability\n\nEnjoy collaborating across research and engineering disciplines\n\nAre comfortable working with large‑scale distributed systems and high‑performance computing\n\nHave experience with training, fine‑tuning, or evaluating large language models\n\nAre adept at analyzing and debugging model training processes\n\nLogistics\nTo participate in the Fellows program, you must have work authorization in the United States, United Kingdom, or Canada and be located in that country during the program.\n\nWe have designated shared workspaces in London and Berkeley where fellows will work from and mentors will visit. We are also open to remote fellows in the United Kingdom, United States, or Canada. We will ask you about your availability to work from Berkeley or London (full‑ or part‑time) during the program.\n\nWe are not currently able to sponsor visas for fellows. To participate, you need to have or independently obtain full‑time work authorization in the United Kingdom, United States, or Canada.\n\nThe program runs for 4 months, full‑time. If you can’t commit to the full duration, please still apply and note your constraints in the application. We review these requests on a case‑by‑case basis.\n\nPlease note: We do not guarantee that we will make any full‑time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit for full‑time roles at Anthropic. In previous cohorts, 25–50% of fellows received a full‑time offer, and we’ve supported many more to go on to do great work on AI safety and security at other organizations.\n\nWe encourage diverse applicants\nWe encourage you to apply even if you do not believe you meet every single qualification. We value representation and strive to include a range of diverse perspectives on our team.\n\n#J-18808-Ljbffr","datePosted":"2026-06-20T04:44:10.892Z","dateModified":"2026-06-20T04:44:10.892Z","hiringOrganization":{"@type":"Organization","name":"NerdLevel Tech","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"1460d19351c5f81ab23bd452"},"url":"https://jobsearcher.com/jobs/1460d19351c5f81ab23bd452"}}