Job 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 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 Why join us? 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 The salary or hourly pay range for this position will be clearly stated in the job posting as required by Massachusetts law. The base pay range for this position is between $155,284.34- $200,000. Base pay will depend on multiple individualized factors including, but not limited to internal equity, job related knowledge, skills and experience. This range represents a good faith estimate of compensation at the time of posting. Boston Dynamics offers a generous Benefits package including medical, dental vision, 401(k), paid time off and an annual bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer for employment.

Requirements

Must-haves

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
Why join us?
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mldockerci/cdkubernetes

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off