Control Systems Engineer — AI Training

Role overview Bring practical control engineering experience to an AI training project involving real physical systems. The work calls for engineers who can connect mathematical models, controller design and measured hardware behaviour. What you’ll be doing • Design and tune controllers for systems such as robots, drones, vehicles or industrial equipment. • Develop first-principles plant models and validate them against observations or measured data. • Use state-space or transfer-function models to support controller decisions and performance improvements. • Work with open-source tools in Python, Julia or Modelica and document your approach clearly. • Collaborate remotely to review results and explain the reasoning behind engineering choices. Who this could suit Control, electrical, mechanical, mechatronics or aerospace engineers who have deployed controllers on physical hardware. Postgraduate study, embedded programming, ROS, system identification, adaptive control or open-source contributions are useful additional experience. What you’ll need Preferred qualifications include a related engineering degree and more than five years of hands-on controller design after graduation. The desired background includes real hardware deployment rather than simulation alone, strong plant-modelling skills, and practical PID experience plus at least one of LQR, MPC or Kalman filtering. Fluent Python and the ability to communicate technical work clearly in English are important. You don’t have to match every requirement exactly. We welcome applicants with different backgrounds, levels of experience and transferable skills. Pay and working arrangements Remote contractor work paying $30–$50 per hour. Weekly hours are not specified. What happens next Apply through Find Jobs in AI with your profile and CV. We’ll review your application and, if you’re a good fit, we’ll be in touch about the next steps.