Staff RL Research Engineer: Scalable Simulation for Robots

Boston Dynamics; Boston (MA); Presencial; USD 155.000 - 200.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical

Dental

401(k)

Paid time off

Annual bonus

Descripción de la vacante

Boston Dynamics seeks a Staff RL Research Engineer to own the RL stack, including massively parallel simulation, domain randomization, policy optimization, and on-robot deployment. You will work with world-class engineers and scientists advancing whole-body control and dexterous manipulation.

The ideal candidate has production-grade RL pipelines, GPU-accelerated physics, and expertise in Python frameworks like PyTorch/JAX.

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

RL research

Production RL pipelines

GPU-accelerated simulation

Domain randomization

Sim-to-real transfer

Kubernetes

Educación

MS or PhD in ML/Robotics

3+ years experience

Herramientas

RSL-RL

CleanRL

RLlib

Stable Baselines

Isaac Gym

MuJoCo

MjWarp

MjLab

PyTorch

JAX

ONNX

TensorRT

Bazel

Docker

CI/CD

Descripción del empleo

Boston Dynamics seeks a Staff RL Research Engineer to own the RL stack, including massively parallel simulation, domain randomization, policy optimization, and on-robot deployment. You will work with world-class engineers and scientists advancing whole-body control and dexterous manipulation.

The ideal candidate has production-grade RL pipelines, GPU-accelerated physics, and expertise in Python frameworks like PyTorch/JAX.