At Toyota Research Institute (TRI), we’re conducting research to amplify human ability, focusing on making our lives safer and more sustainable. Led by Dr. Gill Pratt, TRI’s team of researchers develops technologies to advance AI, robotics, driving, and material sciences. We’re dedicated to building a world of “mobility for all” where everyone, regardless of age or ability, can live in harmony with technology to enjoy a better life. Through innovations in AI, we will:
- Develop technology for vehicles and robots to help people enjoy new levels of independence, access, and mobility.
- Bring advanced mobility technology to market faster.
- Discover new materials that will make batteries and hydrogen fuel cells smaller, lighter, less expensive, and more powerful.
Our work is guided by a dedication to safety– seen in how we research, develop, and validate the performance of vehicle technology to benefit society. As a subsidiary of Toyota, TRI is fueled by a diverse and inclusive community of people who carry invaluable leadership, experience, and ideas from industry-leading companies. Over half of our technical team holds PhD degrees. We’re continually searching for the world’s best talent ‒ people who are ready to define the new world of mobility with us!
We strive to build a company that helps our people thrive, achieve work-life balance, and bring their best selves to work. At TRI, you will have the opportunity to enjoy the best of both worlds ‒ a fun start-up environment with brilliant people who enjoy solving tough problems and the financial backing to successfully achieve our goals. Come work with TRI if you’re interested in transforming mobility through designing safer cars, enabling the elderly to age in place, or designing alternative fuel sources. Start your impossible with us.
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
The long-term vision of TRI’s Accelerated Materials Design and Discovery (AMDD) program is to accelerate the development of truly emissions-free mobility. Realizing this vision will require the discovery of new materials and devices for batteries, fuel cells, and more. Our aim at TRI is to merge cutting-edge computational materials modeling, experimental data, artificial intelligence, and automation to significantly accelerate materials research. Our focus is on developing tools and capabilities to enable this acceleration. We collaborate closely with a dozen universities and national labs and colleagues across global Toyota. AMDD seeks to develop and translate the newest technologies into practice, both within Toyota and the open research community more broadly.
The Internship
We’re looking for a researcher/engineer interested in how machine learning models represent molecules and materials, and whether different models discover the same underlying physical structure. In this role, you’ll collaborate with TRI researchers to investigate how training data, learning objectives, and model architectures shape learned representations. The work will combine mathematical reasoning with computational experiments to study when models arrive at shared representations, what information those representations capture, and how they connect to the physics of the systems being studied.
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Responsibilities
Implement and evaluate machine learning models for molecules and materials using reproducible training and analysis workflows.
Design controlled experiments to study how learned representations vary across training runs, model architectures, and learning objectives.
Investigate the geometry and information content of learned representations and their relationship to physical quantities.
Develop evaluation methods to distinguish meaningful shared structure from similarities introduced by model parameterization or analysis choices.
Contribute high-quality, well-documented code to an internal codebase and help translate results into internal reports, publications, and/or patent disclosures (as appropriate).
Qualifications
Currently enrolled in a doctoral program in materials science, computer science, statistics, applied math, machine learning, physics, or a related discipline.
Strong foundations in machine learning, linear algebra, probability, and optimization, with demonstrated experience training models on real datasets.
Familiarity with representation learning or scientific machine learning; experience studying model convergence, learning dynamics, symmetries, or identifiability is a plus.
Proficiency in Python and modern ML tooling (e.g., PyTorch, experiment tracking, reproducibility best practices).
Ability to work collaboratively across disciplines and to translate research ideas into working code.
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The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, and paid time off benefits (including holiday pay and sick time). Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.
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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.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.
