Control System Engineer

Design and deploy robust control solutions for physical systems while applying your domain expertise to help train next-generation AI systems. This remote contract opportunity centers on translating real-world engineering judgment into high-quality technical input, with no prior AI experience required. Key Responsibilities

• Design and tune PID and advanced controllers, including LQR, MPC, and Kalman filters, for physical systems such as robotics, drones, automotive platforms, or industrial hardware.
• Develop plant models from first principles and validate them against empirical data using state-space and transfer-function methods.
• Implement and verify control algorithms in Python using open-source tools such as python-control, SciPy, CasADi, do-mpc, Julia ControlSystems, and OpenModelica.
• Document engineering decisions and control strategies clearly, and participate in effective technical discussions.
• Analyze system performance, identify improvement opportunities, and iterate designs for optimal real-world operation.
• Collaborate remotely with interdisciplinary contributors while maintaining technical independence.

• Bachelor’s degree or higher in Control, Electrical, Mechanical, Mechatronics, or Aerospace Engineering.
• More than 5 years of hands-on controller-design experience after completing your degree, including deployment on real systems rather than simulation-only work.
• Experience building and validating physical plant models through first-principles and data-driven approaches.
• Demonstrated delivery of classical PID control and at least one modern method, LQR, MPC, or Kalman filtering, on real hardware.
• Python proficiency for control-code development, debugging, and validation with open-source toolchains.
• Exceptional written and verbal English communication skills, with an emphasis on clarity and precision.
• Additional valued experience includes a Master’s or PhD, production MPC using do-mpc or CasADi, Modelica or OpenModelica, Julia, system identification, embedded C/C++, ROS, nonlinear, robust, or adaptive control, and publication or open-source contributions.

• Remote contract engagement.
• Work focuses on real-world control solutions and AI training contributions.

• $30 to $50 per hour.

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