Member of Technical Staff – Senior Engineer, Reinforcement Learning for Wholebody Control
$180,250–$240,250 year
On-siteCambridge, Massachusetts, United States or Cambridge, England, United Kingdom
Job Summary
Design and tune reinforcement learning policies for whole-body control, balancing locomotion and manipulation on a humanoid platform. Integrate learned policies with classical and model-based control techniques to ensure robustness and safety. Own the sim-to-real pipeline, transferring trained models from large-scale GPU simulation to physical robots through domain randomization and system identification. Build and maintain infrastructure to train, evaluate, version, and deploy policies repeatably, bringing them up on hardware and debugging real-world variability. Partner with controls, hardware, and AI teams to define interfaces and harden controllers against environmental limits.
Required Qualifications
- Proven experience developing and training RL policies for continuous control, with strong command of modern RL algorithms and their practical failure modes
- Demonstrated success transferring learned policies from simulation to physical robots and deploying them on real hardware
- Solid grounding in control theory and robot dynamics, and the judgment to combine learned and model-based approaches
- Hands-on experience training policies in GPU-accelerated simulation at scale
- Strong Python and working C++ skills, with the ability to build training and deployment infrastructure that others can rely on
- Experience with whole-body control, locomotion, or manipulation on legged, humanoid, or similarly high-DOF robots
- Track record of taking policies from concept through training, transfer, and field deployment
- Must be able to lift 50 lbs
Desired Qualifications
- Experience with model predictive control, whole-body QP controllers, or trajectory optimization
- Background in system identification, actuator modeling, or contact-rich dynamics
- Experience building ML training infrastructure and experiment-tracking workflows at scale
- Familiarity with imitation learning, teleoperation data, or learning from demonstration
- Publications or demonstrated results in legged locomotion or whole-body control
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