Research Lead, Training Insights

<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>As a Research Lead on the Training Insights team, you'll develop the strategy for, and lead execution on, how we measure and characterize model capabilities across training and deployment. This is a hands-on leadership role: you'll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same.</p> <p>Your work will span the full lifecycle of model development. You'll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You'll also take a cross-organizational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage.</p> <p>This role carries significant visibility and impact. You'll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission.&nbsp;&nbsp;</p> <h2><strong>Responsibilities:&nbsp;</strong></h2> <ul> <li>Build new novel and long-horizon evaluations</li> <li>Develop novel measurement approaches for understanding how model capabilities emerge and evolve during RL training</li> <li>Lead strategic evaluation coverage across the company</li> <li>Shape the evaluation narrative for model releases</li> <li>Lead and mentor a small team of researchers and research engineers, setting research direction and fostering a culture of rigorous, creative research</li> <li>Design evaluation frameworks that balance scientific rigor with the practical demands of production training schedules</li> <li>Build and maintain relationships across Anthropic's research organization to ensure evaluation insights inform training and deployment decisions</li> <li>Contribute to the broader research community through publications, open-source contributions, or external engagement on evaluation best practices</li> </ul> <h2><strong>You may be a good fit if you:&nbsp;</strong></h2> <ul> <li>Have significant experience designing and running evaluations for large language models or similar complex ML systems</li> <li>Have led technical projects or teams, either formally or through sustained ownership of critical research directions</li> <li>Are equally comfortable designing experiments and writing code—you can move between research and implementation fluidly</li> <li>Think strategically about what to measure and why, not just how to measure it</li> <li>Can synthesize information across multiple teams and workstreams to form a coherent picture of model capabilities</li> <li>Communicate complex technical findings clearly to both technical and non-technical audiences</li> <li>Are results-oriented and thrive in fast-paced environments where priorities shift based on research findings</li> <li>Care deeply about AI safety and want your work to directly influence how capable AI systems are developed and deployed</li> </ul> <h2><strong>Strong candidates may also have:&nbsp;</strong></h2> <ul> <li>Experience building evaluations for long-horizon or agentic tasks</li> <li>Deep familiarity with Reinforcement Learning training dynamics and how model behavior changes during training</li> <li>Published research in machine learning evaluation, benchmarking, or related areas</li> <li>Experience with safety evaluation frameworks and red teaming methodologies</li> <li>Background in psychometrics, experimental psychology, or other measurement-focused disciplines</li> <li>A track record of communicating evaluation results to inform high-stakes decisions about model development or deployment</li> <li>Experience managing or mentoring researchers and engineers</li> </ul> <h2><strong>Representative projects:&nbsp;</strong></h2> <ul> <li>Designing and implementing a suite of long-horizon evaluations that test model capabilities on tasks requiring sustained reasoning, planning, and tool use over extended interactions</li> <li>Building systems to track capability development across RL training checkpoints, surfacing insights about when and how specific capabilities emerge</li> <li>Conducting a cross-org audit of evaluation coverage, identifying blind spots, and prioritizing new evaluations to fill critical gaps across Pretraining, RL, Inference, and Product</li> <li>Developing the evaluation methodology and narrative for a major model release, working with research leads and communications to clearly characterize model capabilities and limitations</li> <li>Researching and prototyping novel evaluation approaches for capabilities that are difficult to measure with existing benchmarks</li> <li>Leading a team effort to build reusable evaluation infrastructure that serves multiple teams across the research organization</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>The annual compensation range for this role is listed below.&nbsp;</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>$850,000</span><span class="divider">&mdash;</span><span>$850,000 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:&nbsp;</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 internal job level requirements for the position</p> <p><strong>Location-based hybrid policy:</strong> 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.</p> <p><strong data-stringify-type="bold">Visa sponsorship:</strong>&nbsp;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.</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.&nbsp; 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.<br><br><strong data-stringify-type="bold">Your safety matters to us.</strong> To protect yourself from potential scams, remember that Anthro