Description
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 Energy & Materials, Human-Centered AI, Human Interactive Driving, and Robotics.
This is a summer 2025 paid 12-week internship opportunity with the HID team in the EPIC (Extreme Performance Intelligent Control ) department at TRI. Please note that this internship will be a hybrid in-office role based in Los Altos, CA, and will involve experiments with high-performance vehicles on a closed racetrack.
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, computer vision and optimal control. 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
In this project, we are interested in developing new perception and control 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.
Responsibilities
- Design vision-based models for use in a vehicle autonomy stack
- Develop perception-aware and uncertainty-aware control methodologies
- Implement, deploy, and evaluate models and algorithms on high-performance cars
- Publish your original research at a high impact conference
Qualifications
- Ph.D. candidate working on related fields: computer vision, machine learning, robotics, optimization, or related fields
- Proficiency in at least one of the following: Python, C++, ROS(2), deep learning frameworks such as TensorFlow, TensorRT, and JAX
- Experience in deploying algorithms on real-world platforms
- Excellent collaboration and communication skills
- Strong interest in pushing the limits of vehicle autonomy
Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position.
The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-based roles; however, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. Note that TRI offers a generous benefits package including vacation and sick time. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
Please reference this Candidate Privacy Notice to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information.
TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.
Toyota Research Institute
Artificial Intelligence (AI)
Automotive
Consumer Research
Machine Learning
Product Research
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