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
We are building the systems that let Asimov pick up a box, open a drawer, and operate tools. As a Robotics Researcher in Manipulation, you will develop the grasp planning, contact-rich control, and learned task policies that power Asimov's hands. You will work across model-based control, imitation learning, and reinforcement learning -- with the bar set by whether it works on the physical robot in a real environment, not just in simulation. This role combines research depth with a relentless focus on shipping to hardware.
What You Will Do
Research, develop, and deploy manipulation policies for dexterous task execution on Asimov
Build grasp planning and contact-rich control pipelines that generalize across varied objects and environments
Design and run data collection and teleoperation infrastructure to feed policy training at scale
Train manipulation policies using imitation learning, reinforcement learning, or hybrid approaches -- and iterate until they work in the real world
Integrate manipulation with Asimov's perception stack and broader autonomy pipeline
Systematically diagnose failure modes on hardware and drive improvement
Contribute to open-source releases of manipulation research and tooling
What You Will Bring
Strong foundations in robotics, control theory, and motion planning
Hands-on experience building and deploying manipulation systems on real robotic platforms
Proficiency in Python and C++; experience with PyTorch or JAX
Track record taking manipulation research from prototype to hardware deployment
Experience with data collection infrastructure and teleoperation for policy training
Practical debugging instincts across the full hardware-software stack
Nice to Have
Experience with diffusion policies, transformer-based policy architectures, or large-scale foundation models for manipulation
Prior work on dexterous or in-hand manipulation
Familiarity with contact-rich or deformable object manipulation
Publications at RSS, ICRA, CoRL, or equivalent venues
Why Join Menlo
This is applied robotics research with real stakes -- your code runs on a physical humanoid. We open-source aggressively, so your contributions reach the broader community. You will work alongside researchers and engineers across the full stack, in a team that values shipping over presenting. Competitive compensation and equity.
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.
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