Staff Reinforcement Learning Research Engineer
- 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
- 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
- Built production-grade RL training pipelines
- Deep knowledge of GPU-accelerated physics simulation
- Applied RL to humanoid locomotion, whole-body control, or dexterous manipulation
- Worked on sim-to-real transfer, domain randomization, or system identification
- Experience with heterogeneous compute clusters and Kubernetes
- Ownership of the company wide RL tools powering all of our robots
- Direct access to the compute infrastructure to run large-scale experiments
- The chance to help define what’s possible in real-world robotics
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