Senior Autonomy Controls Engineer Learning-Based Control

You will transition vehicle control from manually tuned MPC to learning-driven policies. You will collect and analyze real-world data, identify plant models, implement learning-based control, integrate controllers safely, define control interfaces, and validate policies before deployment on vehicles.

Responsibilities

• Generate test plans and collect real-world data for plant-model system identification
• Use real-world data to estimate parameters for automatic-control plant models
• Design and implement learning-based control approaches
• Reduce reliance on hand-tuned control parameters using data-driven methods
• Integrate learned controllers into the vehicle-control stack safely and incrementally
• Define interfaces between MPC, PID, state estimation, and learning-based components
• Translate classical-control insights into learning-friendly formulations
• Establish validation criteria for learned control policies before real-vehicle deployment

Requirements

• 2-3 years of experimental data collection and data-analysis experience for automatic-control plant models
• Production-quality software engineering skills in C, C++, or Python
• Deep understanding of modern robotics control systems
• Experience with learning-based control or policy optimization for real-world systems
• Ability to work near hardware and real-time constraints