Research Engineer, Machine Learning (RL Velocity)

## About the Role The Research Engineer position at Anthropic is a unique opportunity to work on the development of reliable, interpretable, and steerable AI systems. As a member of the RL Velocity team, you will be responsible for building and improving the core platform that underpins the company's RL science stack. This includes identifying and removing bottlenecks, debugging, and rearchitecting where needed. Your work will have a direct impact on the efficiency and reliability of Anthropic's RL training infrastructure, enabling researchers to iterate quickly on training runs.

The RL Velocity team is a quickly growing group of committed researchers, engineers, and business leaders working together to build beneficial AI systems. As a Research Engineer, you will partner closely with researchers and adjacent engineering teams to understand pain points and ship tooling that makes them faster. You will also own the reliability and performance of research runs end-to-end and contribute to design decisions that shape how Anthropic does RL at scale.

## What You Will Do - Build and improve the RL training infrastructure that researchers depend on day-to-day - Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed - Partner closely with researchers and with adjacent engineering teams to understand pain points and ship tooling that makes them faster - Own the reliability and performance of research runs end-to-end - Contribute to design decisions that shape how Anthropic does RL at scale - Develop and maintain large-scale distributed training systems - Collaborate with cross-functional teams to ensure seamless integration of RL systems - Troubleshoot and resolve complex technical issues - Develop and implement automated testing and validation procedures - Stay up-to-date with the latest developments in ML and RL research

## What We Are Looking For - Strong software engineering fundamentals and a track record of building performant, reliable systems - Experience with ML infrastructure, distributed systems, or research tooling - A passion for enabling other people's work and finding leverage through platforms rather than individual experiments - Comfort operating across the stack, from low-level performance work to RL algorithms - A bias toward shipping and iterating quickly, with a mix of high agency and low ego - Excellent communication and collaboration skills - Ability to work in a fast-paced environment and adapt to changing priorities - Strong problem-solving skills and attention to detail

## Nice to Have - Experience with large-scale distributed training (RL, pre-training, or post-training) - Familiarity with JAX, PyTorch, or similar ML frameworks - A track record of operating at the edge of research and infra in a fast-moving environment

## Benefits and Perks - Competitive salary and equity package - Opportunity to work on cutting-edge AI research and development - Collaborative and dynamic work environment - Flexible work arrangements, including remote work options - Professional development and growth opportunities - Access to the latest tools and technologies - Comprehensive health and wellness benefits - Generous paid time off and holiday policy

- To stand out as a candidate, make sure to highlight your experience with ML infrastructure and distributed systems in your resume and cover letter.

• Be prepared to discuss your approach to debugging and troubleshooting complex technical issues during the interview process.
• Familiarize yourself with Anthropic's research and values to demonstrate your passion for the company's mission and vision.
• Showcase your ability to work collaboratively and communicate effectively with cross-functional teams.
• Consider creating a portfolio or GitHub repository to demonstrate your coding skills and experience with ML frameworks.
• Be prepared to negotiate your salary and benefits package based on your experience and qualifications.
• Research the company culture and values to ensure alignment with your own goals and expectations.