At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.
This is a paid 12-week internship opportunity and is a hybrid, in-office role.
Here’s a glimpse into the Internship experience from some of our TRI interns!
The Team
Within the Extreme Performance Intelligent Control team, we are developing techniques to advance vehicle safety and performance capabilities using tools from optimization, machine learning, and computer vision. We are pursuing a cooperative approach to high-performance driving by developing predictive models and control frameworks for extreme vehicle maneuvering that match or exceed the skills of expert drivers. We envision a world where these tools allow each vehicle to support its driver, creating a safe and enjoyable experience for all.
The Internship
We are looking for a research intern to develop new optimization, embodied reasoning, control, modeling, and/or perception methodologies for high-performance driving. This is an opportunity to apply your knowledge to novel problems and develop impactful research. This paid, 12-week, Summer 2026 internship is a hybrid in-office role in our headquarters in Los Altos, CA, and will involve experiments with high-performance vehicles on a closed racetrack.
Investigate how machine learning models represent molecules and materials, designing experiments to compare learned representations and connect their geometry and information content to underlying physical properties.
Develop and validate perception, embodied reasoning, modeling, and control methods for high-performance driving, deploying algorithms on vehicles and presenting research findings internally and at international conferences.
Conduct computational and machine learning research on human-robot interaction, developing and validating data-efficient, robust approaches for collaboration, shared autonomy, and embodied interaction.
Research multiscale physics-based battery models and automated scientific reasoning, using experimental data and materials knowledge to benchmark and extend models for new battery materials.
Conduct original research in machine learning and autonomous driving, advancing world models through representation learning, action conditioning, and evaluation, with prototypes tested in closed-loop simulation.
