Researcher, Robot Learning

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.

Asimov’s promise is a humanoid that learns: one that discovers new skills and gets better the more it works. You’ll build the learning systems that make that true.

As a Researcher in Robot Learning on our Brain team, you’ll own the methods that turn data into general-purpose behavior on Asimov. Our locomotion and manipulation researchers push specific skills; you’ll work on the layer underneath and across them. That means generalist policies, the data that trains them, and the loops that let Asimov improve from its own experience. You’ll train on real robot data, ship to real hardware, and open-source what you can.

• Own Asimov’s generalist policy research end to end, taking each idea from a hypothesis and a dataset to a trained policy running on the physical robot

• Train and post-train vision-language-action models and other generalist policies that follow instructions and transfer across tasks and environments

• Build imitation learning pipelines on teleoperation and demonstration data, and figure out which data actually moves performance

• Design the self-improvement loop, covering autonomous data collection, success detection, and learning from deployment, so Asimov gets better the more it runs

• Combine learning from demonstrations, reinforcement learning, and simulation to close the sim-to-real gap at scale

• Define the evaluations that tell us whether a policy is genuinely better on real hardware, not just on a benchmark

• Partner with World Models, Locomotion and Manipulation, and Platform so your research lands in the stack that ships, and agents calling Asimov through our SDK have capable skills underneath them

• Publish, open-source, and share what you learn with the community building on Asimov

• You have deep hands-on experience training learning-based robot policies, through imitation learning, reinforcement learning, or both

• You’ve taken a learned policy from research code to a physical robot and dealt with everything that broke along the way

• You’re fluent in Python and PyTorch, and comfortable training at scale on multi-GPU clusters

• You think in data: you know how dataset composition, quality, and diversity shape what a policy can and can’t do

• You have a PhD or equivalent research experience in robot learning, ML, or a related field, or a portfolio that speaks for itself

• You communicate clearly through papers, blogs, talks, or well-documented code

• You use AI as a real collaborator in your research

• Experience with VLA models or robot foundation models such as OpenVLA, Octo, or π0

• Work with diffusion policies, flow matching, or action chunking

• Experience with autonomous data collection, reward learning, or continual learning on real robots

• Time on humanoids or other high-DoF, whole-body platforms

• Familiarity with large-scale simulation in Isaac Lab, MuJoCo, or mjlab

• Published work at CoRL, RSS, ICRA, NeurIPS, or similar venues

• Contributions to open-source robot learning libraries such as LeRobot

Most robot learning research stops at a paper. Here, your policies run on a humanoid in short cycles, the data pipeline is yours to shape, and your work ships in the open to a community already building on Asimov. You’ll be working on the question that decides whether humanoids become useful at all: can a robot learn new work on its own? If you want to answer that on real hardware, come build it with us.

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.

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.

Originally posted on Himalayas