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 building the motion intelligence that lets Asimov walk, recover, climb stairs, and carry loads without falling over. As a Robotics Researcher in Locomotion, you will work on the Cyclotron team -- Menlo's locomotion training pipeline -- developing the controllers and learned policies that run on physical bipedal hardware. You will train in simulation, close the sim-to-real gap, and deploy to the robot. The bar is real-world robustness, not benchmark performance.
What You Will Do
Research, develop, and iterate on locomotion controllers and motion policies for a bipedal humanoid
Train and evaluate policies in Uranus, Menlo's in-house simulation engine, across a wide range of behaviors including walking, recovery, stair climbing, and load-bearing
Design reward functions, curriculum schedules, and training infrastructure that produce policies robust enough for real-world deployment
Drive systematic sim-to-real transfer and hardware iteration
Integrate locomotion outputs with the broader Asimov autonomy stack
Collect and analyze hardware telemetry to guide policy improvement
Contribute to open-source releases of locomotion research and Cyclotron tooling
What You Will Bring
Strong foundations in reinforcement learning, optimal control, and rigid body dynamics
Hands-on experience training and deploying locomotion or motion control policies on physical legged robots
Proficiency in Python; strong experience with JAX or PyTorch
Experience with physics simulation environments such as MuJoCo, Isaac Gym, Genesis, or equivalent
Practical track record closing the sim-to-real gap on a real platform
Ability to iterate fast, instrument failures, and make data-driven improvements
Nice to Have
Prior work specifically on bipedal or humanoid locomotion
Experience with whole-body control, model predictive control, or loco-manipulation
Familiarity with motion capture or real-time state estimation pipelines
Publications at RSS, ICRA, CoRL, or equivalent venues
Why Join Menlo
This is applied robotics research with real stakes -- your code runs on a physical humanoid. We open-source aggressively, so your contributions reach the broader community. You will work alongside researchers and engineers across the full stack, in a team that values shipping over presenting. Competitive compensation and equity.
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.
