Human Interactive Driving Research Intern, Learning-Based Control for Safe Autonomous Driving

Within the Extreme Performance Intelligent Control team, we are developing techniques to advance vehicle safety and performance capabilities using tools from machine learning, optimization, and control theory. We pursue a hybrid approach to high-performance driving that unites physics-based optimal control with data-driven learning. By embedding vehicle dynamics and optimization structure into learned controllers, we aim to build systems that adapt to uncertainty and match or exceed expert drivers while retaining the interpretability and guarantees of classical methods.

We are seeking a motivated intern to join our team, focusing on cutting-edge research combining machine learning with physical system knowledge and control theory to develop reliable controllers for autonomous vehicles. Potential research areas include designing learning-based control methods that enforce forward invariance, stability, and constraint satisfaction; and incorporating vehicle dynamics and other physical knowledge into learning and control amid challenging driving scenarios.

This role provides an exciting opportunity to explore approaches to machine learning for control that support safe, intuitive, and responsive driving systems. Along the way you will apply your knowledge to tackle novel research questions and work towards a publication with professional researchers.  The internship will be in our headquarters in Los Altos, CA and includes competitive compensation befitting the challenging, but fun, nature of the research work at TRI. Applicants with relevant publications in the fields above and good collaboration skills are highly encouraged to apply.

Work with researchers to develop and then undertake a well-scoped research project in a relevant area.

Extend existing controls/AI/ML algorithms or design new algorithms for control at a vehicle’s performance limit.

Explore approaches to formalize guarantees of safety for machine learning algorithms.

Implement algorithms on a full scale vehicle research platform.

Publish your original research at a high impact conference.

Ph.D. or M.S. student in relevant fields - ML/AI, robotics, computer science, mechanical engineering, aerospace engineering, or similar.

Proficient in scientific Python, Unix, and robotic frameworks (e.g. ROS2.0).

Knowledge of machine learning, nonlinear system control, and optimal control. Vehicle dynamics knowledge is a plus.

Experience working on joint scientific and engineering projects.

Familiarity with or interest in racing or high-performance driving is a plus.