Research Engineer, Code RL (Reinforcement Learning)

Anthropic•San Francisco, CA

9h•$500,000 - $850,000•Hybrid

About The Position

We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to write, edit, test, debug, and ship real software — end to end, on real codebases, with real tools — and to do it correctly, fast, and safely. This role blends research and engineering. You'll design RL environments and coding tasks, build the reward signals and verifiers that capture what "good code" means, run training experiments on frontier models, diagnose why a model does (or doesn't) get better at a class of software-engineering work, and improve the speed and reliability of the pipelines that make all of that iterate fast. Code RL spans several focus areas — from agentic coding behaviors and code correctness, to long-horizon autonomous engineering, to high-performance code for accelerators — and we'll match you to the area where you'll have the most impact.

Requirements

Strong software-engineering skills
Deep Python expertise, including async/concurrent programming
Comfortable owning systems end to end and debugging across the stack
Ability to balance research exploration with engineering implementation
Ability to engage rigorously in shaping experimental design and interpreting results
Care about code quality, testing, and performance
Passionate about the potential impact of AI and committed to developing safe and beneficial systems

Nice To Haves

Experience with reinforcement learning, RLHF, post-training, or LLM finetuning
Built coding agents, code-execution sandboxes, eval harnesses, verifiers, or developer tooling
Background in program analysis, testing, verification, compilers, or formal methods
Experience with PyTorch and large-scale distributed training
Experience with performance profiling and optimization of ML systems
CUDA / GPU or TPU kernel experience and accelerator-performance intuition
Experience with virtualization and sandboxed code execution environments

Responsibilities

Design RL environments and coding tasks
Build reward signals and verifiers that capture what "good code" means
Run training experiments on frontier models
Diagnose why a model does (or doesn't) get better at a class of software-engineering work
Improve the speed and reliability of pipelines for fast iteration