Senior / Staff Robotics Engineer, Manipulation

RoboForce; Milpitas (CA); On-site; Full time

Benefits offered by this job

Competitive stock options

Health, dental, and vision insurance

401(k) plan

Visa sponsorship and green card support

Regular team-building events

Job summary

An AI robotics company is seeking a Senior Robotics Engineer to lead the arm autonomy stack. The role involves designing algorithms for safe object manipulation and advanced motion planning. Ideal candidates will have a Ph.D. or an M.S. with significant experience in robotics, along with strong C++ skills and expertise in control theory. The position offers competitive equity, health benefits, and a collaborative work environment in Milpitas, California.

Qualifications

• Experience with high-DOF arm motion in dynamic environments.
• Experience implementing PID, Impedance Control, or MPC.
• Knowledge in reinforcement learning for manipulation tasks.

Responsibilities

• Architect algorithms for arm motion planning and trajectory generation.
• Develop low-level control strategies for safe interactions.
• Build the logic for remote manipulation capabilities.

Skills

Rigid body kinematics

Modern C++ (C++17/20)

Motion planning libraries

Control Theory

Applied Mathematics

Education

Ph.D. in Robotics or related field

M.S. degree with 4 years experience

Tools

Eigen

KDL

Motion planning libraries (OMPL, MoveIt)

Job description

Overview

Why RoboForce 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.

We are seeking Senior 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.

Responsibilities

• Advanced Motion Planning & Trajectory Generation: You will architect the algorithms that drive high-DOF arm motion in dynamic environments. This involves implementing constrained motion planners that generate smooth, jerk-limited trajectories in real-time. You will also solve the “Whole Body” control problem, coordinating the mobile base and the manipulator arm simultaneously to extend the robot’s workspace and maintain stability during heavy lifting tasks.
• Interaction Control & Compliance: You will develop the low-level control strategies that enable the robot to physically interact with the world safely. This includes designing and tuning impedance/admittance controllers that allow the robot to modulate stiffness based on force feedback—essential for tasks like opening heavy doors or plugging in connectors.
• Teleoperation & Human-Robot Interface: You will build the mathematical engine behind our remote manipulation capabilities. You are responsible for the logic that translates human operator inputs into feasible, safe robot motions. You will design the control mapping and assistance layers that ensure remote manipulation feels intuitive and stable, managing the complex translation between human intent and the robot’s physical constraints without compromising safety.

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.

Bonus Qualifications

• 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.
• ML for Control: Interest or experience in Reinforcement Learning (RL) or Learning from Demonstration (LfD) for manipulation tasks.
• Real Actuators: Experience working with industrial communication buses (EtherCAT/CAN) and motor drives.

Benefits

• Competitive stock options/equity programs.
• Health, dental, and vision insurance, 401(k) plan.
• Visa sponsorship and green card support for qualified candidates.
• Lunches and dinners, a fully stocked kitchen, and regular team-building events.