We are looking for Control System Engineer candidates for a project delivered through the hiring partner.
What you'll do
- 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.
- Develop plant models from first principles and validate them against empirical data using state-space and transfer function methodologies.
- Implement and verify control algorithms in Python using open source toolkits (e.g., python-control, SciPy, CasADi, do-mpc, Julia ControlSystems, OpenModelica).
- Document engineering decisions and control strategies with clear, high-quality written communication, and engage in effective verbal discussions as required.
- Analyze system performance, identify areas for improvement, and iterate on design for optimal real-world operation.
- Collaborate remotely with interdisciplinary contributors while maintaining autonomy and technical independence.
What you need
- Proven real system deployment (not simulation-only).
- Proficiency in building and validating physical plant models using first principles and data-driven methods.
- Demonstrated delivery of both classical (PID) and at least one modern control method (LQR, MPC, or Kalman) on real hardware.
- Fluency in Python for control code development, debugging, and validation using open source stacks.
Nice to have
- Bachelor’s degree (or higher) in Control, Electrical, Mechanical, Mechatronics, or Aerospace Engineering.
- 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.
Expertise
RequiredAlso useful
Who you work with
Project and contracting process: the hiring partner. Applications continue on the provider's website.