Control System Engineer

Role Title: Control System Engineer

Role Type: Contractor

Location: Remote

The company is engaging Control System Engineers to contribute their advanced technical expertise to an exciting customer project focused on real-world control solutions. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. This opportunity is ideal for professionals passionate about shipping robust controllers, modeling complex plants, and applying both classical and modern control strategies in tangible environments.

Scope of Work

1. Design and tune PID and advanced controllers (e.g., LQR, MPC, Kalman Filters) for deployment on physical systems such as robotics, drones, automotive, or industrial hardware.
2. Develop plant models from first principles and validate them against empirical data using state-space and transfer function methodologies.
3. Implement and verify control algorithms in Python using open source toolkits (e.g., python-control, SciPy, CasADi, do-mpc, Julia ControlSystems, OpenModelica).
4. Document engineering decisions and control strategies with clear, high-quality written communication, and engage in effective verbal discussions as required.
5. Analyze system performance, identify areas for improvement, and iterate on design for optimal real-world operation.
6. Collaborate remotely with interdisciplinary contributors while maintaining autonomy and technical independence.

Preferred Qualifications

1. Bachelor’s degree (or higher) in Control, Electrical, Mechanical, Mechatronics, or Aerospace Engineering.
2. Over 5 years of hands-on controller design experience post-degree, with proven real system deployment (not simulation-only).
3. Proficiency in building and validating physical plant models using first principles and data-driven methods.
4. Demonstrated delivery of both classical (PID) and at least one modern control method (LQR, MPC, or Kalman) on real hardware.
5. Fluency in Python for control code development, debugging, and validation using open source stacks.
6. Exceptional written and verbal English communication skills, focused on clarity and precision.
7. Additional strengths such as a Master’s or PhD, production MPC with tools like do-mpc or CasADi, Modelica/OpenModelica modeling, Julia proficiency, system identification, embedded C/C++, ROS, nonlinear/robust/adaptive control, or contributions to publications/open source are valued.