Robotics Engineer, Humanoid System Integration

Integrate humanoid subsystems into a reliable full-stack robot system running on physical hardware.

Define and maintain interfaces across autonomy, manipulation, locomotion, sensors, compute, controls, and field tools.

Own robot bring-up, calibration, configuration, diagnostics, and validation workflows.

Debug system-level issues across software, hardware, networking, timing, compute constraints, sensor data, and robot behavior.

Develop integration tests, regression tests, telemetry tools, dashboards, and release-readiness checks.

Partner with manipulation, hardware, QA/QC, field application, and service teams to keep humanoid platforms continuously shippable.

Harden research or prototype capabilities into maintainable, testable, deployment-ready modules.

MS or PhD in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field preferred; BS considered with a demonstrated track record of hands-on robotics work across multiple physical systems — research projects, competition robotics, or internships with daily hardware exposure.

Background appropriate for a junior-to-mid engineer; fresh MS and PhD graduates welcome.

Strong hands-on experience integrating robotic systems on real hardware — must include physical hardware; simulation-only backgrounds will not be considered.

Strong C++ and Python skills in Linux-based robotics environments.

Experience with ROS/ROS2, robot middleware, drivers, calibration, diagnostics, logging, and distributed systems.

Understanding of perception, planning, controls, locomotion, manipulation, sensor integration, and embedded compute constraints.

Ability to profile performance, isolate failures, define interfaces, and coordinate across multiple technical teams.

You've integrated a humanoid or legged/mobile manipulation platform from bring-up through field deployment — not just research or simulation.

You've owned the interfaces between locomotion, manipulation, and perception, and debugged failures across those seams on real hardware.

You've built the tooling that made other engineers faster: calibration pipelines, regression harnesses, telemetry dashboards, or release gates that prevented bad deploys.

You carry hard-won instincts about what separates a demo-ready system from a deployment-ready one — and you've acted on that distinction.