Research Engineer, Machine Learning (RL Velocity)
Anthropic · Remote | San Francisco, CA | New York City, NY · AI Research & Engineering
About this role
Anthropic is hiring a mid-level Research Scientist in the machine learning function as a remote position. The posting calls out experience with PyTorch, Reinforcement Learning, Distributed Systems, Data Structures. Compensation is listed at $500,000–$850,000 per year.
- Role
- Research Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Remote | San Francisco, CA | New York City, NY
- Work mode
- Remote
- Department
- AI Research & Engineering
More roles at Anthropic
Job description
from Anthropic careersAbout Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run.
Responsibilities
- 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 (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster
- Own the reliability and performance of research runs end-to-end