Robotics AI Engineer

About MenloMenlo 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 RoleAs a Robotics AI Engineer, you will work at the intersection of learning and hardware, training and deploying policies that run on Asimov in the real world. This is not a research role in the traditional sense. You will be expected to get results on physical robots, not just in simulation, and to iterate fast when things break. You will work closely with the hardware, firmware, and infrastructure teams to close the loop between training and deployment.What You Will DoDesign and train RL and imitation learning policies for locomotion, manipulation, or whole-body controlRun experiments on physical hardware and close the sim-to-real gap through systematic debugging and domain adaptationBuild and maintain simulation environments and data pipelines that support fast policy iterationInstrument robot deployments and analyze failure modes to feed improvements back into trainingCollaborate with hardware and firmware engineers to understand physical constraints and improve policy robustnessWhat We Are Looking ForStrong foundations in reinforcement learning or imitation learning, with hands-on experience training policies that run on real systemsComfort working directly with robots, not just simulatorsProficiency in Python and familiarity with standard RL/ML frameworks (JAX, PyTorch, IsaacGym/IsaacLab, MuJoCo, or similar)An empirical, debugging-first mindset, you care about what actually works on hardwareAbility to move fast and context-switch between research problems and engineering tasksNice to HavePrior work on humanoid or legged robot platformsExperience with sim-to-real transfer techniques (domain randomization, system identification, noise injection)Contributions to open-source robotics projectsBackground in control theory, trajectory optimization, or dynamicsWhy Join MenloThe policies you train do not sit in a notebook. They run on a real humanoid, in the real world, on short feedback loops. You will see your work move physical hardware within days, not quarters. You will collaborate directly with the hardware, firmware, and infrastructure teams, with high visibility and real stakes, and everything you can open-source, you will. If you want to build the systems that turn software into physical labor, this is where it happens.