Anthropic Research Resident

<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><h2><strong>About the role</strong></h2> <p>The Anthropic Residency is our paid, fixed-term research program, with placements lasting between six weeks and six months. We are looking for people who have led ambitious work in a variety of technical fields and want to transition to full-time work focused on making sure AI systems are safe and beneficial for all of humanity. If you are a <strong>professor in a quantitative discipline, a technical founder or a staff-level engineer</strong> and want to transition to work on transformative AI, we want to hear from you. Background in machine learning is not required.</p> <h2><strong>What to expect</strong></h2> <ul> <li>A concrete, scoped project matched to your background, with direct mentorship from Anthropic researchers</li> <li>Access to Claude models and GPU compute</li> <li>Connection to the broader AI safety and security research community</li> </ul> <h2><strong>Interview process</strong></h2> <p>The interview process will include an initial application, recruiter screen, technical assessment, research discussion and a reference check.</p> <p>Applications for this round close on November 22, 2026 (11:59 PM PT). We expect to make offers in January 2027.</p> <h2><strong>You may be a good fit if you</strong></h2> <ul> <li>Are motivated by making sure AI is safe and beneficial for society as a whole</li> <li>Are excited to transition into working on empirical AI research full-time</li> <li>Have led ambitious work in a variety of technical fields (professor in a quantitative discipline, a technical founder or a staff-level engineer)</li> <li>Thrive in fast-paced, collaborative environments</li> <li>Can implement ideas quickly and communicate clearly</li> </ul> <h2><strong>Strong candidates may also</strong></h2> <ul> <li>Have experience authoring research papers in machine learning, NLP, or AI safety</li> <li>Have experience with LLMs, reinforcement learning or Kubernetes clusters and complex shared codebases</li> </ul> <h2><strong>Mentors and research areas</strong></h2> <p>Potential Anthropic mentors include:</p> <ul> <li><a href="https://www.linkedin.com/in/sara-price-310757b2/">Sara Price</a>: leads the Alignment Training team; background in data science</li> <li><a href="https://ethanperez.net/">Ethan Perez</a>: leads the Alignment Science team; background in machine learning</li> <li><a href="https://evhub.github.io/">Evan Hubinger</a>: leads the Alignment Stress-Testing team; formerly at The Machine Intelligence Research Institute (MIRI)</li> <li><a href="https://scholar.google.com/citations?user=fW7yK10AAAAJ&hl=en">Samuel Marks</a>: leads the Scalable Oversight team; background in mathematics</li> <li><a href="https://elanapearl.github.io/">Elana Simon</a>: researches the interpretability of language models and biological models; background in biomedical informatics</li> <li><a href="https://belindal.github.io/">Belinda Li</a>: researches how language models represent the world, users, and themselves; background in natural language processing (PhD, MIT)</li> <li><a href="https://jlindsey15.github.io/">Jack Lindsey</a>: leads the Model Psychology team; background in theoretical neuroscience</li> <li><a href="https://nicholas.carlini.com/">Nicholas Carlini</a>: researches adversarial machine learning and AI security; previously at DeepMind and Google Brain</li> <li><a href="https://sohldickstein.com/">Jascha Sohl-Dickstein</a>: works at the intersection of safety and pretraining; background in physics and neuroscience, formerly Principal Scientist at Google Brain</li> </ul> <p>Residents will work on empirical research questions in collaboration with Anthropic's research teams. Areas we are particularly interested in include:</p> <p>AI safety & alignment</p> <ul> <li>Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains.</li> <li>Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.</li> <li>Model Organisms: Creating <a href="https://www.lesswrong.com/posts/ChDH335ckdvpxXaXX/model-organisms-of-misalignment-the-case-for-a-new-pillar-of-1">model organisms of misalignment</a> to improve our empirical understanding of how alignment failures might arise.</li> <li>Model Internals and Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures.</li> <li>Safeguards Research: Developing robust defenses against adversarial attacks, comprehensive evaluation frameworks for model safety, and automated systems to detect and mitigate potential risks before deployment.</li> <li>AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations.</li> </ul> <p>AI security & frontier red-teaming</p> <ul> <li>Stress-testing frontier models to understand the full extent of their current capabilities in fields such as cybersecurity, robotics or cryptography.</li> </ul> <p>ML systems & reinforcement learning</p> <ul> <li>Improving how frontier models are trained and building the infrastructure behind that research, from model architecture, optimizers and training data to accelerator simulators, hardware backends and RL environment pipelines.</li> </ul> <p>For a fuller picture of the questions we care about, see our <a href="https://alignment.anthropic.com/2025/recommended-directions/">Recommendations for Technical AI Safety Research Directions</a> and recent publications from our <a href="https://alignment.anthropic.com/">Alignment</a>, <a href="https://transformer-circuits.pub/">Interpretability</a> and <a href="https://red.anthropic.com/">Frontier Red</a> teams.</p> <h2><strong>Logistics</strong></h2> <ul> <li><strong>Duration:</strong> Placements are six weeks, three months or six months, depending on team fit and candidate preferences. Other lengths between three and six months are possible by exception.</li> <li><strong>Location:</strong> We prefer that Residents work from our San Francisco office, where most of our research staff is based, but remote participation is possible, especially for shorter placements. Most full-time research roles at Anthropic require relocation to San Francisco. Remote participants need to be based in a country where Anthropic can hire.</li> <li><strong>Commitment:</strong> Full-time role for the duration of the residency.</li> <li><strong>Compensation:</strong> $350,000 to $500,000/year pro-rated for the duration of the residency + benefits</li> <li><strong>Visa sponsorship:</strong> We sponsor visas for placements of three months or longer. For six-week placements we encourage remote participation from a country where Anthropic can hire.</li> </ul> <p>Applicants may also be considered for the <a href="https://job-boards.greenhouse.io/anthropic/jobs/5183044008">Anthropic Fellows Program</a>.</p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>The annual compensation range for this role is listed below. </p> <p>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.</p></div><div class="title">Annual Salary:</div><div class="pay-range"><span>$350,000</span><span class="divider">—</span><span>$500,000 USD</span></div></div></div><div class="content-conclusion"><h2><strong>Logistics</strong></h2> <p><strong>Minimum education: </strong>Bachelor’s degree or an equivalent combination of education, training, and/or experience</p> <p><strong>Required field of study: </strong>A field relevant to the role as demonstrated through coursework, training, or professional experience</p> <p><strong>Minimum years of experience: </strong>Years of experience required will correlate with the interna