System Identification & Controls Engineer

Responsibilities

• Plan and run system identification across all Skild robot platforms — actuators, transmissions, joints, rigid-body dynamics, and sensors.
• Design the excitation trajectories and bench/on-robot tests, and know which experiment answers which question.
• Characterize actuators and motors on dynamometers, test benches, and hardware-in-the-loop setups, alongside the EE, ME, and firmware teams.
• Fit dynamics models, quantify their accuracy, and close the sim-to-real gap against our simulators.
• Apply classical controls — state estimation, calibration, stability and bandwidth analysis — to real hardware.
• Build automated pipelines that scale identification from a single robot to the whole fleet.
• Quantify unit-to-unit variation, track drift and wear over time, and flag outlier units.
• Set the standard and tooling for system identification at Skild, and document findings rigorously.

Preferred Qualifications

• MS or PhD in Mechanical/Electrical Engineering, Controls, Robotics, Aerospace, or a related field — or equivalent hands-on experience.
• A demonstrated, hands-on track record of system identification on real robotic or electromechanical hardware — identified and validated on physical systems, not just in simulation.
• A strong classical controls foundation: feedback/feedforward and cascade control, frequency-response and stability analysis, state estimation and Kalman filtering.
• A solid grasp of robot hardware and mechatronics: motors and field-oriented control, transmissions, encoders, IMUs, and force-torque sensors.
• Practical experience with excitation design, hardware data collection, and parameter estimation (time- and frequency-domain methods).
• Proficiency in Python and C++ in a Linux environment; MATLAB/Simulink a plus.
• Familiarity with robotics dynamics tooling and simulators (MuJoCo, Isaac Sim, Drake, Pinocchio, ROS/ROS2).
• Experience deploying calibration or controls across a large fleet of robots or vehicles is highly valued.