Job description
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
You’ll work on the perception and learning stack that lets Asimov do things in the real world.
What You'll Do
Design and train perception and representation models for whole body control
Contribute to our world model research (SSL, predictive representations)
Evaluate models against actual robot performance
Work across the locomotion, autonomy, and hardware teams to ship behaviors that hold up outside the lab
Open-source what we can and help set the bar for how whole-body control is done in the open
Track and apply ideas from the broader AI literature pool
What We Look For
Computer vision foundations: solid grounding in computer vision, working with real (not just curated) visual data
Representation learning: hands-on experience with self-supervised methods, e.g. contrastive learning, masked prediction, JEPA-style objectives, embeddings/encoders
ML/LLM breadth: comfortable reading and applying ideas across LLMs and general ML, not siloed to robotics
Has trained models before: real end-to-end experience, e.g. data pipelines, training runs, debugging, evaluation, not just papers
Sim-to-real experience: has taken a model from simulation onto a physical robot and dealt with the gap firsthand.
Fluency in Python or C++, and comfort living in a simulator (IsaacSim, MuJoCo, or similar)
Nice to Have
Published or open-source work in locomanipulation, whole-body control, or sim-to-real transfer
Experience with contact-rich or bimanual manipulation
Familiarity with humanoid platforms and real-time control on embedded hardware
Background in imitation learning, teleoperation, or large-scale robot data collection
Why Join Menlo
You will be part of a tight-knit team teaching a humanoid to do real physical work, not demos. You will have genuine ownership of the full stack of technologies that decides what Asimov can do in the world, and you will see your work run on real robots in weeks, not years. If you want locomanipulation to mean something beyond a benchmark, this is the place to build it.
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.
Originally posted on Himalayas
Who can apply
Eligible countries: Singapore, United States. Accepted UTC offsets: UTC-10, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC+8, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.