menlo - Researcher, Locomotion
Requirements
• 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
Responsibilities
• 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
Benefits
• You will own locomotion for a humanoid that thousands of developers are building on in the open. Your policies will ship to real robots, not slides, and the community will see them walk. If you want your research to leave the lab and stand up on its own two feet, this is the place. • You don't need deep AI expertise for every role, but we do expect everyone at Menlo to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement. When that's the case, we'll say so explicitly in the qualifications. People who thrive here don't treat AI as a novelty. They use it to think better, and make their work easier for others to build on. • Equal Opportunity and Accommodations
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