Robotic Controls Engineer

mundane; Palo Alto (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

mundane is seeking an experienced robotics control engineer to own the control stack for teleoperated humanoid robots. You will translate human input into stable robot motion, design cross-platform control strategies, and work hands-on with real hardware from research prototypes to field deployment.

You will collaborate with controls, hardware, and learning teams to optimize latency, stability, and operator feel, extending URDF models and debugging across multi-DoF systems.

Qualifications

• Degree in a robotics/engineering field with hands-on hardware experience.
• Experience with multi-DoF robotic systems and real hardware debugging.
• Strong foundation in control systems and robot dynamics.

Responsibilities

• Own the design and development of control strategies translating human input into robot motion.
• Maintain intuitive control across platforms with varying configurations.
• Build and tune multi-DoF control systems on real manipulators.
• Evaluate impedance and other control approaches for deployment.
• Define test protocols and acceptance criteria for stability.
• Develop control interfaces bridging research algorithms to hardware.
• Work with URDF models and integrate software with hardware, debugging issues.
• Mentor interns and engineers moving approaches to deployment.

Skills

Python

C++

Robot kinematics

Control theory

Education

Bachelor's or Master's in Robotics, Mechanical, Electrical Engineering or CS

Tools

URDF

ROS/ROS2

Gazebo

Job description

You'll own control systems for teleoperated humanoid robots, translating human input into stable, real-world robotic behavior. You'll work on human-in-the-loop control systems where operator input must adapt to robotic platforms with different physical constraints, designing control strategies that maintain intuitive operation while ensuring system stability and performance across configurations.

This is a hands-on role working directly with real hardware. You'll partner with controls, hardware, and learning teams to develop and deploy algorithms on physical systems, prioritizing real-world performance over simulation, and take primary ownership of the control stack from research prototype through field deployment.

Responsibilities

• Own the design and development of control strategies that translate human input into coordinated robot motion
• Architect methods to maintain intuitive control across systems with different physical configurations, and set technical direction for how the team approaches cross-platform control
• Build, tune, and maintain multi-DoF control systems on real robotic manipulators
• Design, implement, and evaluate advanced control approaches (e.g., impedance-style control) and drive decisions on which techniques to adopt
• Ensure stable, consistent system behavior under varying dynamics, including defining test protocols and acceptance criteria
• Build and optimize control interfaces connecting research algorithms to hardware, balancing latency, stability, and operator feel
• Work with and extend URDF models to represent robot structure and constraints accurately
• Integrate, test, and debug systems on real robots, including root-causing hardware/software interaction issues
• Collaborate with cross-functional teams to move approaches from research to deployment, and mentor interns or junior engineers contributing to the control stack

Qualifications

• Bachelor's or Master's degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field
• 2+ years of hands-on experience with multi-DoF robotic systems (e.g., robotic arms such as UR or Franka, multi-fingered hands, or multi-limb/humanoid platforms) in a professional or research setting
• Strong understanding of robot kinematics (forward/inverse kinematics, Jacobians) for multi-DoF systems
• Solid grounding in control systems (PID required; hands-on experience with impedance or force control strongly preferred)
• Working understanding of robot dynamics (mass, inertia, torque relationships) and how they affect control tuning across many coupled joints
• Proficiency in Python and/or C++, with experience writing production-quality, maintainable control code
• Experience with URDF and robot modeling concepts, including debugging model-to-hardware mismatches on multi-DoF systems
• Demonstrated track record of debugging real physical systems, not just simulated ones

Bonus

• Experience with teleoperation systems, haptics, or human-in-the-loop control
• Familiarity with ROS / ROS2 and robotics tooling (RViz, Gazebo, etc.)
• Exposure to biomechanics, human motor control, or perception
• Prior experience taking a control system from research prototype to field-deployed product on a multi-DoF or humanoid platform