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# Staff Reinforcement Learning Research Engineer

- **Company:** Boston Dynamics
- **Location:** Waltham, MA, United States
- **Experience:** Experienced
- **Salary:** $155,284.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Continuous Integration, Data Visualization, Machine Learning, Reinforcement Learning, Pytorch, Kubernetes, ONNX (Open Neural Network Exchange) Format, Data Analytics, TensorRT, Docker
- **Published:** May 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=7ed621d671ad5574

## About the Role

Do you have experience in Simulation systems?, * 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 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

## 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


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