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.

• 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.

• Bachelor’s degree (or higher) in Control, Electrical, Mechanical, Mechatronics, or Aerospace Engineering.
• Over 5 years of hands-on controller design experience post-degree, with proven real system deployment (not simulation-only).

Work arrangement: Fully remote