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. 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.
A Note on AI
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
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.
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Build and scale the infrastructure for post-training open-weight models, including distributed training and rollouts, agent environments, evaluation tools, and reliable production serving in close partnership with researchers.
Develop robotics capabilities in multimodal foundation models, spanning visual and spatial reasoning, robotics data pipelines, embodied planning, and evaluation in collaboration with foundation-model researchers and robotics partners.
Build and own infrastructure for post-training open-weight models, including distributed training and rollouts, agent environments, evaluation tools, and reliable production integration in close partnership with researchers.
