Research Engineer, Domain Scaling

About the Role The Domain Scaling team focuses on making Claude world‑class at real‑world knowledge work in domains like finance, healthcare, and legal. This unique role blends applied research with data sourcing to enhance our models. You will own the end‑to‑end process of creating reinforcement learning environments for new capabilities: identifying high‑value tasks, designing reward signals, managing vendor relationships, and measuring impact on model performance. Responsibilities Own the data strategy for knowledge work verticals end‑to‑end, from task sourcing through RL training. Manage technical relationships with external data vendors, including evaluation of data quality and reward design. Collaborate with domain experts to design data pipelines and evaluations. Explore novel ways of creating RL environments for high‑value tasks. Develop and improve QA frameworks to catch reward hacking and ensure environment quality. Run generalization experiments to measure how data strategy changes improve model capabilities. Partner with other RL research teams and product teams to translate capability goals into training environments and evaluations. Required Qualifications Experience with fine‑tuning large language models for specific domains or real‑world use cases. Experience with reinforcement learning, reward design, or training data curation for LLMs. Comfortable managing technical vendor relationships and iterating quickly on feedback. Strong cross‑functional collaboration skills. Passion for making AI more useful and accessible across different industries. Excitement about a role that combines applied research and hands‑on data work. Preferred Qualifications Experience training production ML systems. Experience designing evals or benchmarks for LLMs. Domain expertise in a vertical where we want to make our models more useful. Experience working with external vendors or technical partners. Compensation

$350,000 – $850,000 USD

Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience. Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience. Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position. Location‑based hybrid policy: We expect all staff to be in one of our offices at least 25% of the time; some roles may require more time in our offices. Visa sponsorship: We do sponsor visas and will make every reasonable effort to obtain a visa if an offer is extended. #J-18808-Ljbffr Anthropic