Researcher, Locomotion

About this role

About Menlo Menlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable, turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can.

If you want your work to matter beyond a paper or a demo, this is the place. The Role 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 What We Look For - 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 Why Join Menlo You will own locomotion for a humanoid that thousands of developers are building on in the open.