Anthropic
Research Engineer, Machine Learning (RL Velocity)
About this role
Anthropic seeks a Research Engineer to build and optimize the reinforcement learning infrastructure that powers the organization's AI research. You'll focus on removing performance bottlenecks, improving training systems reliability, and enabling faster iteration across the research team.
What you'll do
- Develop and enhance RL training infrastructure used by researchers
- Identify and eliminate performance bottlenecks through debugging and profiling
- Collaborate with research and engineering teams to build tools addressing pain points
- Ensure reliability and end-to-end performance of research experiments
- Contribute to architectural decisions for large-scale RL systems
What they're looking for
- Software engineering fundamentals and systems design
- ML infrastructure or distributed systems experience
- Performance optimization and profiling
- ML frameworks (JAX, PyTorch, or similar)
- Full-stack development across algorithms and low-level optimization
- Ability to operate in fast-paced research environments
- Cross-functional collaboration and communication
Benefits
- Competitive annual compensation: $500,000–$850,000 USD
- Remote-friendly with offices in San Francisco, NYC (25% minimum in-office)
- Visa sponsorship support available
- Work on cutting-edge AI safety and reliability research
- Collaborate with top researchers and engineers
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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 describe a time when you identified and removed a significant bottleneck in an ML or distributed systems infrastructure? What was your approach to profiling and debugging?
- Tell us about your experience building or improving RL training infrastructure. What were the key performance or reliability challenges you faced?