Research Engineer, Environment 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><strong>About the role</strong></h2> <p>The Environment Scaling team is a team of researchers and engineers whose goal is to improve the intelligence of our public models for novel verticals and use cases. The team builds the training environments that fuel RL at scale. This is a unique role that combines executing directly on ML research, data operations, and project management 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><strong>Responsibilities:</strong></h2> <ul> <li>Improve and execute our fine-tuning strategies for adapting Claude to new domains and tasks</li> <li>Manage technical relationships with external data vendors, including evaluation of data quality and reward design</li> <li>Collaborate with domain experts to design data pipelines and evaluations</li> <li>Explore novel ways of creating RL environments for high value tasks</li> <li>Develop and improve QA frameworks to catch reward hacking and ensure environment quality</li> <li>Partner with other RL research teams and product teams to translate capability goals into training environments and evals</li> </ul> <h2><strong>You may be a good fit if you:</strong></h2> <ul> <li>Have experience with fine-tuning large language models for specific domains or real-world use cases and/or domain expertise in an area where we would like to make our models more useful.</li> <li>Have experience with reinforcement learnin