Anthropic Fellows Program, The Anthropic Institute (Economics & Policy) at Anthropic

<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>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.</p></div><h1><strong>Anthropic Fellows Program overview</strong></h1> <p>The 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.</p> <p>Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on project aligned with our research priorities, with the goal of producing a <strong>public output</strong> (e.g. a paper submission). In one of our earlier cohorts, over 80% of fellows produced papers. We run multiple cohorts of Fellows each year and review applications on a rolling basis.</p> <p><u>Submit your application to the Economics & Policy workstream at the bottom of this page.</u> We are accepting applications on a rolling basis for the next cohort expected to start in January 2027. In some circumstances, we can accommodate fellows starting outside the usual cohort timelines — please note in your application if the January 2027 start date doesn't work for you.</p> <h2><strong>What to expect</strong></h2> <ul> <li>4 months of full-time research </li> <li>Direct mentorship from Anthropic researchers </li> <li>Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (these vary by country)</li> <li>Funding for compute (~$15k/month) and other research expenses</li> </ul> <h2><strong>Interview process</strong></h2> <p>The interview process will include an initial application & reference check, assessments & interviews, and a research discussion. </p> <p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong> 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.</p> <h2><strong>Compensation</strong></h2> <p>The 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 (with possible extension).</p> <h1><strong>The Anthropic Institute Fellows (Economics & Policy)</strong></h1> <h2><strong>Mentors, research areas, & past projects</strong></h2> <p>Fellows will undergo a project selection & mentor matching process. Potential research areas and mentors include:</p> <ul> <li><a href="https://www.anthropic.com/economic-futures">Economics</a></li> <ul> <li>Maxim Massenkoff</li> <li>Peter McCrory</li> </ul> <li><a href="https://www.anthropic.com/news/the-anthropic-institute">Policy, Security, and Society</a></li> <ul> <li>Jack Clark</li> <li>Marina Favaro</li> <li>Jim Baker</li> </ul> </ul> <p>Projects in this workstream may include:</p> <ul> <li>Designing and conducting empirical research on AI's economic effects, drawing on external data sources</li> <li>Developing new methodological approaches for studying AI's impact on labor markets, the future of work, and society</li> <li>Analysing the offense–defense balance for AI-enabled cyber and bio capabilities as models scale</li> <li>Measuring the extent to which model performance increases with custom harnesses</li> <li>Identifying market driven mechanisms that could improve societal resilience to anticipated threats from AI systems</li> <li>Identifying which metrics relating to AI R&D could serve as early warning signals for recursive self-improvement</li> </ul> <p>For past project examples, see:</p> <ul> <li&

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