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# Research Engineer, Domain Scaling

- **Company:** Anthropic Limited
- **Location:** San Francisco, CA, United States
- **Salary:** $2,080.0 - $4,160.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Reinforcement Learning, Large Language Models, Data Strategy, Machine Learning Operations, Data Pipelines
- **Published:** June 21, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a798266e38cc31a6

## About the Role

Do you have experience in Vendor relationship building?, Do you have a Bachelor's degree?, * Have experience with fine-tuning large language models for specific domains or real-world use cases

 * Have experience with reinforcement learning, reward design, or training data curation for LLMs
 * Are comfortable managing technical vendor relationships and iterating quickly on feedback
 * Find value in reading through datasets to understand them and spot issues
 * Have strong cross-functional collaboration skills
 * Are passionate about making AI more useful and accessible across different industries
 * Are excited about a role that includes a combination of applied research and hands-on data work

Strong candidates may also

 * Have experience training production ML systems
 * Have experience designing evals or benchmarks for LLMs
 * Have domain expertise in a vertical where we would like to make our models more useful
 * Have experience working with external vendors or technical partners, 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: 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.

## Description

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., * 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 envs for high value tasks
 * Develop and improve QA frameworks to catch reward hacking and ensure env 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 envs and evals


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