Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we’re looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
The Role
At Mind Robotics, we’re building generalized physical AI — robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Controls is where intelligence becomes motion: the layer that translates high-level commands into precise, safe, and coordinated physical behavior across every joint and actuator on the robot.
We’re looking for a Controls Engineer to own the algorithms and software that make our robots move well — from low-level actuator loop design to full-body motion control and the simulation infrastructure that keeps us iterating fast.
Responsibilities
•
Design, implement, and tune control algorithms — PID, state-space, model-based, and beyond — for joints, actuators, and whole-body robot motion.
•
Develop and maintain high-fidelity dynamic models of robot subsystems to support simulation, analysis, and controller design.
•
Analyze real-world robot data to assess controller performance, identify regressions, and drive targeted improvements.
•
Work closely with firmware engineers to implement control algorithms under hard real-time constraints in C/C++ or Rust.
•
Collaborate with mechanical and electrical engineers to characterize hardware, close the loop between physical design and software performance, and define actuation requirements.
•
Define and execute test plans that validate control system performance across the full operating envelope of the robot.
Qualifications
•
M.S. or Ph.D. in controls, robotics, or a related field, or equivalent hands-on experience building and deploying control systems for real physical hardware.
•
Demonstrated experience (through work, research, or projects) designing and deploying control systems for real physical systems — robots, actuators, drones, or similar.
•
Strong foundation in control theory: classical (PID, lead-lag), modern (state-space, LQR/MPC), and familiarity with nonlinear systems.
•
Experience building dynamic models and using simulation tools (MATLAB/Simulink, Python, Julia, or equivalent) to inform and validate controller design.
•
Hands-on experience tuning controllers on real hardware and debugging unexpected behavior with real data.
•
Proficiency in C/C++ or Rust for real-time control implementation.
•
You are comfortable with ambiguity, move fast, and have an “engineering curiosity” that drives you to understand how the entire system works, not just your part.
Nice to Have
•
Experience with whole-body control, trajectory optimization, or model predictive control on legged or manipulator systems.
•
Familiarity with field-oriented control (FOC) or other motor control algorithms at the firmware level.
•
Exposure to functional safety standards (ISO 26262, IEC 61508, or similar).
•
Experience with ROS2 or similar robotics middleware.