Robotics Researcher, Manipulation - Menlo

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 researcher