Robotics Engineer, Humanoid System Integration

Job description

Job responsibilities

• 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.

Job requirements

• 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.