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Human Interactive Driving Intern Uncertainty-Aware Control
Human Interactive Driving Intern Uncertainty-Aware ControlToyota Research Institute • Los Altos, CA, United States
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Human Interactive Driving Intern Uncertainty-Aware Control

Human Interactive Driving Intern Uncertainty-Aware Control

Toyota Research Institute • Los Altos, CA, United States
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Human Interactive Driving Intern Uncertainty-Aware Control

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 in Automated Driving, Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavioral Models, and Robotics.

This is a Summer 2026 paid 12-week internship opportunity. Please note that this internship will be a hybrid in-office role.

The Team

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 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 control, modeling, and / or perception methodologies for high-performance driving that infer and reason about uncertainty. 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.

Responsibilities

  • Develop novel uncertainty-aware control, modeling, or / and perception algorithms.
  • Implement, deploy, and evaluate models and algorithms on high-performance cars.
  • Present results in verbal and written communications at international conferences and internally.

Qualifications

  • Ph.D. candidate working on related fields : optimization, machine learning, control, Bayesian modeling, computer vision, or related fields.
  • Proficiency in at least one of the following : Python, C++, ROS(2), Pytorch, JAX.
  • Experience in deploying algorithms on real-world platforms.
  • Excellent collaboration and communication skills. Strong interest in pushing the limits of vehicle autonomy.
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