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 As 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 Do - Design and train RL and imitation learning policies for locomotion, manipulation, or whole-body control - Run experiments on physical hardware and close the sim-to-real gap through systematic debugging and domain adaptation - Build and maintain simulation environments and data pipelines that support fast policy iteration - Instrument robot deployments and analyze failure modes to feed improvements back into training - Collaborate with hardware and firmware engineers to understand physical constraints and improve policy robustness What We Are Looking For - Strong foundations in reinforcement learning or imitation learning, with hands-on experience training policies that run on real systems - Comfort working directly with robots, not just simulators - Proficiency 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 hardware - Ability to move fast and context-switch between research problems and engineering tasks Nice to Have - Prior work on humanoid or legged robot platforms - Experience with sim-to-real transfer techniques (domain randomization, system identification, noise injection) - Contributions to open-source robotics projects - Background in control theory, trajectory optimization, or dynamics Why Join Menlo The 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. 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.