Research Engineer, Code RL (Reinforcement Learning)

About the Role

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.

Qualifications

You may be a good fit if you:

• Have strong software-engineering skills and deep Python expertise, including async/concurrent programming
• Are comfortable owning systems end to end and debugging across the stack
• Can balance research exploration with engineering implementation, and engage rigorously in shaping experimental design and interpreting results
• Care about code quality, testing, and performance
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems

Strong candidates may also have:

• 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; 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

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Hybrid policy: Staff are expected to be in one of our offices at least 25% of the time, with some roles requiring more time in office.
• Visa sponsorship: We sponsor visas and make every reasonable effort with an immigration lawyer.