Research Engineer, Domain Scaling

<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 class="heading">About the role</h2> <p>The Domain Scaling team has the goal to make Claude world-class at real-world knowledge work in domains like finance, healthcare, and legal. This is a unique role that combines executing directly on applied research and data sourcing (real-world and synthetic) to improve our models. You'll own the end-to-end process of creating RL environments for new capabilities: identifying high-value tasks, designing reward signals, managing vendor relationships, and measuring impact on model performance.</p> <h2 class="heading">Responsibilities</h2> <ul> <li> <p>Own the data strategy for knowledge work verticals end-to-end, from task sourcing through RL training</p> </li> <li> <p>Manage technical relationships with external data vendors, including evaluation of data quality and reward design</p> </li> <li> <p>Collaborate with domain experts to design data pipelines and evaluations</p> </li> <li> <p>Explore novel ways of creating RL envs for high value tasks</p> </li> <li> <p>Develop and improve QA frameworks to catch reward hacking and ensure env quality</p> </li> <li> <p>Run generalization experiments to measure how data strategy changes improve model capabilities</p> </li> <li> <p>Partner with other RL research teams and product teams to translate capability goals into training envs and evals</p> </li> </ul> <h2 class="heading">You may be a good fit if you</h2> <ul> <li> <p>Have experience with fine-tuning large language models for specific domains or real-world use cases</p> </li> <li> <p>Have experience with reinforcement learning, rewa