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.
Research Engineer, Domain Scaling en Anthropic
Híbrido - San Francisco, CA; New York City, NY; Seattle, WA
Más vacantes en AnthropicSalary
USD 350,000 - 850,000
Requirements
Skills
- Bachelor’s degree or an equivalent combination of education, training, and/or experience
- 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
- Value in reading through datasets to understand them and spot issues
- Strong cross‑functional collaboration skills
- Passionate about making AI more useful and accessible across different industries
- Excited about a role that includes a combination of applied research and hands‑on data work
- Experience training production ML systems (strong candidates)
- Experience designing evals or benchmarks for LLMs (strong candidates)
- Domain expertise in a vertical where we would like to make our models more useful (strong candidates)
- Experience working with external vendors or technical partners (strong candidates)
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
Technologies
Reinforcement LearningLarge Language Models
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