Staff Reinforcement Learning Research Engineer

What you'll do

• Implement learning algorithms and scale GPU-accelerated simulation; make the training pipeline fast, reliable, and reproducible.
• Transfer learned policies to physical robots and integrate RL with VLAs to fine-tune and distill multimodal policies.
• Build deployment and visualization tools for data-driven robotics research.

What they ask for

• 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 and simulation and rendering tooling.
• Proficient in PyTorch and/or JAX, inference runtimes, and software fundamentals including Bazel, monorepos, Docker, and CI/CD.

Nice to have

• Built production-grade RL training pipelines and has deep knowledge of GPU-accelerated physics simulation.
• Applied RL to humanoid locomotion, whole-body control, or dexterous manipulation; experience with sim-to-real transfer, domain randomization, system identification, heterogeneous compute clusters, or Kubernetes.

Pay

USD 155,284.34- $200,000 base salary

Tools

RSL-RL, CleanRL, RLlib, Stable Baselines, Isaac Lab, MuJoCo, MjWarp, MjLab

AI focus

Develop reinforcement learning for robotics, including policy training and optimization, sim-to-real transfer, on-robot deployment, and integration with VLAs to fine-tune and distill large multimodal policies.