System Identification & Controls Engineer

At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.

We're hiring a System Identification & Controls Engineer to characterize, model, and validate the dynamics of every robot we work with — as accurately as possible, and at fleet scale. This is a senior individual-contributor role for someone who has done rigorous system identification on real robots before and walks in already knowing which tests to run.

• 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.
  
  

• 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.
• Strong classical controls foundation: feedback/feedforward and cascade control, frequency-response and stability analysis, state estimation and Kalman filtering.
• 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.