Senior Autonomy Controls Engineer – Learning-Based Control

About the Role

Own the transition from manually tuned MPC-based vehicle control to learning-driven control policies that adapt across vehicles with minimal human intervention, while maintaining safety and interpretability.

Core Responsibilities

• Practical understanding of vehicle dynamics and system identification
• Practical experience in generating test plans, collecting real-world data, and using real-world data for system identification of plant models for automatic control.
• Design and implement learning-based control approaches (imitation learning, reinforcement learning, hybrid MPC + learning)
• Reduce dependence on hand-tuned control parameters through data-driven methods
• Integrate learned controllers into the existing vehicle control stack safely and incrementally
• Define interfaces between classical control (MPC, PID, state estimation) and learning-based components
• Work closely with the Principal Controls Engineer to translate classical control insights into learning-friendly formulations
• Establish validation criteria for learned control policies before real-vehicle deployment

Required Qualifications

• 2-3 years of experience with experimental data collection and data analysis to estimate parameters of a plant model used for automatic control
• Strong software engineering skills in C, C++, or Python (production-quality code)
• Deep understanding of modern robotics control systems
• Experience with learning-based control or policy optimization for real-world systems
• Comfort working close to hardware and real-time constraints

Preferred Qualification

• Reinforcement learning or imitation learning for control
• Model-based RL, residual learning, or hybrid MPC architectures
• Control under uncertainty and partial observability
• Debugging and validating control systems on physical platforms

Bonus Points

• Experience deploying learned controllers on vehicles or mobile robots
• Familiarity with safety-constrained learning methods
• Background spanning both classical and modern control theory