Researcher, Locomotion

We are looking for a Researcher, Locomotion to push Asimov from walking to running, recovering, and moving through the real world with the confidence of something alive. Asimov 0 was built to learn locomotion and Asimov 1 to learn whole-body control, and you will own the policies that make that motion robust on real hardware. You will not stop at a clip in simulation. You will get your work onto a physical biped and keep pushing until it holds up under contact, disturbance, and terrain we did not train for.

What You’ll Do

• Design, train, and ship reinforcement learning policies for bipedal and whole-body locomotion on Asimov
• Own the sim2real pipeline end to end, building on our zero-shot sim2real work so a first run on hardware is never really a first run
• Push balance, gait, and recovery behavior past the demo stage into something that survives pushes, slips, and uneven ground
• Build and refine reward design, domain randomization, and training environments in MuJoCo
• Close the loop between simulation and hardware with real telemetry from real robots, then feed what breaks back into the next policy
• Work shoulder to shoulder with hardware, controls, and manipulation researchers, since whole-body control does not respect team boundaries
• Open-source what you can and write up what you learn so the community can build on it

• Deep hands-on experience with reinforcement learning for continuous control, legged locomotion, or whole-body control
• A track record of getting learned policies onto real robots, not only into papers or simulators
• Strong command of a physics simulator such as MuJoCo, Isaac, or similar, including reward shaping and domain randomization
• Fluency in Python and modern RL tooling, and comfort in a ROS2-based control stack
• A bias for shipping: you would rather see a policy stumble on real hardware this week than look perfect in sim next month
• Clear thinking about why a policy fails, not just whether it does

Nice to Have

• Published work in locomotion, legged robotics, or sim2real transfer
• Experience with model predictive control or classical locomotion methods alongside learning-based approaches
• Contributions to open-source robotics or RL projects
• Experience bringing up new hardware and debugging the messy gap between a model and a motor

We hire talented people from a wide range of backgrounds. If you’re excited about a role but don’t meet every bullet, we still encourage you to apply. Menlo Research is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Menlo provides reasonable accommodations during the application process. If you need one, please let your recruiter know.