Menlo - Robotics AI Engineer
Requirements
• 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 • 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 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.
Responsibilities
• 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
Benefits
• 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. • 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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