Controls Engineer

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond.

We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.

JOB SUMMARY

Apptronik is building robots for the real world — to improve human quality of life and help solve the ever-increasing labor shortage. As a Controls Engineer – Actuation on our Controls team, you will own both the actuator models that make our simulation trustworthy and the actuator-level control that runs on real hardware — closing the loop from bench characterization and system identification, through simulator integration, to controller design, tuning, and deployment. You will pair deep motor electromechanics and system-identification skill with strong C++/Python engineering, working hand in hand with our Controls, Firmware, and Simulation teams so that both model fidelity and control performance are driven by how they are actually used on the robot.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

• Develop and maintain high-fidelity actuator models in simulation, capturing the physical behaviors (friction, compliance, thermal, torque and current limits) that meaningfully affect real-world performance.
• Design, tune, and deploy actuator-level control — closed-loop torque and current control and the low-level control that turns commands into reliable joint behavior on hardware.
• Design and run hardware test campaigns to characterize actuator behavior and extract the parameters that keep both the models and the controllers accurate.
• Apply system identification to extract trustworthy physical models from experimental data, and use those models to inform controller design and gain selection.
• Quantitatively validate sim-to-real fidelity — for both the models and the controllers — using appropriate comparison metrics, and understand the limitations of each.
• Build and maintain robust Python/C++ tooling for experiment automation, model extraction, controller tuning, and simulator integration.
• Partner with the Controls team throughout — aligning on the fidelity and control performance actually needed, how model and controller choices ripple into training and policy performance, and where the actuator layer is the root cause of a sim-to-real gap.
• Support hardware bring-up and deployment, translating validated models and controllers into real robot configuration.