Robotics Researcher, Navigation

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 understand where it is and decide where to go.

As a Robotics Researcher in Navigation, you will own the full stack from state estimation and mapping through global and local planning -- closing the loop between perception outputs and motion execution in dynamic, unstructured real-world environments.

This is an applied research role.

You will train and deploy in simulation (Uranus), validate on physical hardware, and iterate until it works in the real world.

What You Will Do Research, develop, and deploy navigation algorithms for bipedal humanoid robots operating in complex indoor environments Own localization, mapping, and SLAM pipelines capable of running in real time on embedded compute Build planning frameworks that account for the physical constraints of a legged platform -- footstep planning, terrain traversal, dynamic obstacle avoidance Integrate navigation with Asimov's broader autonomy stack including perception and locomotion Develop data collection and evaluation infrastructure to benchmark performance across environments Systematically close the sim-to-real gap using Uranus and hardware iteration Contribute to open-source releases of navigation research and tooling What You Will Bring Strong foundations in estimation theory, probabilistic robotics, and motion planning Proven track record building and deploying SLAM, planning, or autonomous navigation systems on real robots Proficiency in Python and C++; familiarity with ROS or equivalent middleware Experience getting systems to work end-to-end -- not just in simulation Ability to debug across the hardware-software boundary and move fast on ambiguous problems Nice to Have Prior work on legged or humanoid navigation specifically Experience with learning-based planning or visuomotor navigation policies Familiarity with neural map representations or semantic scene understanding Publications at ICRA, IROS, CoRL, RSS, 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.