RoboForce
Senior / Staff Robotics Engineer, Motion Planning
$160K–$265K
Milpitas, CA +1 · Staff · Full-time · Posted Jan 19, 2026
- Location
- Milpitas, CA
Milpitas, California, United States - Experience
- M.S. degree with 4+ years of relevant industry experience is one stated education path; the posting does not specify a separate general years-of-experience requirement.
- Education
- Ph.D. in Robotics, Mechanical Engineering, Computer Science, or a related field, OR an M.S. degree with 4+ years of relevant industry experience.
- Visa sponsorship
- Visa sponsorship and green card support for qualified candidates.
About the job
Own the robot fleet’s local mobility and safety stack, converting AI-generated routes and costmaps into safe, dynamically feasible motion for physical robots on uneven terrain.
What you'll do
- Develop local motion planners and trajectory optimization for heavy mobile robots on uneven terrain.
- Build real-time C++ pipelines that convert AI perception outputs into planner constraints, with deterministic safety and fallback behaviors.
- Account for changing center of mass and connect base trajectories and constraints to the Whole-Body Control architecture.
What they ask for
- Hands-on experience developing and tuning advanced local planners and trajectory optimization for mobile robots in complex, unstructured environments.
- Strong understanding of kinematics, vehicle dynamics, and center-of-mass constraints for top-heavy mobile robots on uneven outdoor terrain.
- Expertise in modern C++ (C++17/20), including real-time performance, memory management, and low-latency execution.
- Track record deploying autonomous navigation stacks on physical robotic platforms.
Nice to have
- Familiarity with Whole-Body Control frameworks, operational space control, and mobile-base/manipulator coordination.
- Experience with commercial SLAM/VIO, slip or traction estimation, or CUDA acceleration.
Pay
$160,000–$265,000 USD base salary
Tools
C++17/20, MPC, MPPI, SLAM, VIO, CUDA, Whole-Body Control (WBC)
AI focus
Integrate AI-generated routes, semantic maps, and learned costmaps into motion planning, with a deterministic safety layer that can override unsafe AI inputs.
Model-written summary · the original posting is authoritative