- Robot type
- Industrial Automation
- Location
- MilpitasCaliforniaUSA
- Job type
- Hardware
- Posted
- Jan 19, 2026
- Salary
- $160,000–$265,000 a year
Senior / Staff Robotics Engineer, Manipulation
This job has closed.
RoboForce is no longer taking applications for this role. See open roles at RoboForce or browse similar open jobs.
Job description
We are seeking Senior / Staff Robotics Engineer, Manipulation to own the arm autonomy stack. You will architect the algorithms that allow our robot to reach, grasp, and manipulate objects safely and intelligently. You will bridge the gap between high-level planning and low-level control, ensuring our robot can interact with the world with both precision and compliance.
RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company’s robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.
Job responsibilities
- Advanced Motion Planning & Trajectory Generation: You will architect the algorithms that drive high-DOF arm motion in dynamic environments.
- Interaction Control & Compliance: You will develop the low-level control strategies that enable the robot to physically interact with the world safely.
- Teleoperation & Human-Robot Interface: You will build the mathematical engine behind our remote manipulation capabilities.
Job requirements
- Experience: Ph.D. in Robotics, Mechanical Engineering, Computer Science, or related field, OR an M.S. degree with 4+ years of relevant industry experience.
- Applied Mathematics: Deep understanding of rigid body kinematics, dynamics, and 3D geometry. You are fluent in linear algebra and Lie theory for representing robot state.
- Modern C++ Expertise: You write production-grade, real-time C++. You are comfortable with modern standards (C++17/20) and libraries like Eigen, KDL, or Pinocchio.
- Planning Frameworks: Experience with motion planning libraries (e.g., OMPL, MoveIt, TrajOpt, or similar).
- Control Theory: Practical experience implementing PID, Impedance Control, or Model Predictive Control (MPC) on real hardware.
- Requires 5 days/week in-office collaboration with the teams.
- Physics Simulation: Experience tuning physics parameters in simulators like Isaac Sim, MuJoCo, or Gazebo to match real-world dynamics.
- Optimization: Familiarity with solvers like OSQP, Ipopt, or Ceres for trajectory optimization.
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