Anthropic
Research Engineer, Domain Scaling
About this role
Join Anthropic's Domain Scaling team to develop specialized AI capabilities for real-world industries like finance, healthcare, and legal. You'll lead the end-to-end process of creating reinforcement learning environments, managing data sourcing, and measuring model performance improvements across knowledge work domains.
What you'll do
- Own data strategy for knowledge work verticals from task sourcing through RL training
- Manage technical relationships with external data vendors and evaluate data quality
- Collaborate with domain experts to design data pipelines and evaluation frameworks
- Develop novel approaches for creating RL environments for high-value tasks
- Build QA frameworks to prevent reward hacking and ensure environment quality
- Partner with RL and product teams to translate capability goals into training environments
What they're looking for
- Large language model fine-tuning and domain adaptation
- Reinforcement learning and reward design
- Training data curation and LLM dataset management
- Vendor management and technical relationship building
- ML evaluation and benchmark design
- Cross-functional collaboration
- Production ML systems experience
- Domain expertise in finance, healthcare, or legal
Benefits
- Annual salary range: $350,000–$850,000 USD
- Hybrid work policy requiring 25% office time minimum
- Visa sponsorship available
- Work on cutting-edge AI safety and capability research
- Multiple office locations: San Francisco, New York City, or Seattle
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Can you walk us through a project where you fine-tuned or adapted a large language model for a specific domain or real-world use case? What challenges did you encounter?
- Describe your experience with reinforcement learning and reward design. How have you approached designing reward signals that avoid reward hacking?