Anthropic Fellows Program, ML Systems & Reinforcement Learning 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 an empirical 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>Apply at the bottom of this page. 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>Access to a shared workspace </li> <li>Connection to the broader AI safety and security research community</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, technical 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>ML Systems & Performance Fellows</strong></h1> <h2><strong>Mentors, research areas, & past projects</strong></h2> <p>Fellows will undergo a project selection & mentor matching process. Potential mentors include:</p> <ul> <li>Alwin Peng</li> <li>Zygi Straznickas</li> </ul> <p><em>Note: You may research mentors' prior work, but all applications must go through the official form, not the mentors.</em></p> <p>For a past example of an engineering-heavy project, see:</p> <ul> <li><a href="https://red.anthropic.com/2025/smart-contracts/">AI agents find $4.6M in blockchain smart contract exploits</a></li> </ul> <p>Projects in this workstream may include:</p> <ul> <li>Building a CPU simulator for accelerator workloads</li> <li>Adding backends for different accelerators on an open source project</li> <li>Building on demand infrastructure for other infrastructure heavy fellows projects </li> <li>Building complex synthetic data or environment pipelines</li> </ul> <h2><strong>Unique candidate criteria</strong></h2> <p>You might be a particularly great fit for this workstream if you:</p> <ul> <li>Have strong software engineering skills with experience building complex ML systems</li> <li>Can balance research exploration with engineering rigor and operational reliability</li> <li>Enjoy collabo

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