Staff Reinforcement Learning Research Engineer

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Description

Do you want to build the scalable reinforcement learning framework that powers the next generation of humanoid and quadruped robots? As a Staff RL Research Engineer, you'll own the RL stack, including massively parallel simulation, domain randomization, policy optimization, and on-robot deployment. Your job is to make the pipeline fast, reliable, and reproducible. You'll work alongside world-class engineers and scientists pushing the boundaries of whole-body control and dexterous manipulation. In this role, you will: Implement on-policy and off-policy learning algorithms Scale GPU-accelerated simulation to generate millions of samples per second Crack sim-to-real to produce policies that transfer to the physical robot Integrate RL with VLAs to fine-tune and distill large multimodal policies Make deployment easy, fast, and reproducible Build visualization tools that enable data-driven research Required Qualifications MS with 3+ years of experience, or PhD, in ML, Robotics, or a related field Deployed policies on physical robots with attention to latency, robustness, and safety Expertise with RL toolboxes (RSL-RL, CleanRL, RLlib, Stable Baselines) Expertise with simulation and rendering tooling (Isaac Lab, MuJoCo, MjWarp, MjLab) Proficient in PyTorch and/or JAX, plus inference runtimes (ONNX, Triton, TensorRT) Solid software fundamentals: Bazel, monorepos, Docker, CI/CD The ideal candidate has: Built production-grade RL training pipelines Deep knowledge of GPU-accelerated physi

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