INTERNSHIP DETAILS

Accelerated Science Intern, Representation Learning for Molecules and Materials

CompanyToyota Research Institute
LocationLos Altos
Work ModeOn Site
PostedNovember 5, 2026
Internship Information
Core Responsibilities
The intern will develop and evaluate algorithms for multi-agent learning and coordination to improve predictive modeling and computational efficiency. They will also design benchmarks and contribute high-quality code to internal research projects.
Internship Type
intern
Salary Range
$45 - $65
Company Size
471
Visa Sponsorship
No
Language
English
Working Hours
40 hours
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About The Company
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.
About the Role

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 are seeking a research intern to develop algorithms and computational systems for multi-agent learning and coordination. The central question is how agents with different data and capabilities can share information, build predictive models, and select computational tasks that improve their collective understanding of data. Potential approaches include distributed and federated learning, as well as information-theoretic methods for communication and task selection. The intern will implement and benchmark these methods, evaluating how coordination affects model accuracy, communication costs, and computational efficiency. Relevant interests include machine learning, multi-agent systems, distributed computing, and information theory.

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Responsibilities
  • Implement and evaluate algorithms for learning and coordination among agents with different data and computational capabilities.

  • Prototype methods for sharing information, updating predictive models, and selecting computational tasks.

  • Design controlled benchmarks comparing coordination strategies with independent-agent and centralized approaches.

  • Evaluate model accuracy, communication costs, and computational efficiency using reproducible experimental workflows.

  • 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 computer science, machine learning, statistics, applied math, electrical engineering, or a related discipline.

  • Strong foundations in machine learning, probability, and optimization, with demonstrated experience implementing and evaluating algorithms.

  • Familiarity with multi-agent systems, distributed or federated learning, or information theory.

  • Proficiency in Python and modern ML tooling (e.g., PyTorch, experiment tracking, reproducibility best practices); experience with distributed computing is a plus.

  • 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.

 

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.

 

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.

Key Skills
Machine learningPythonPyTorchMulti-agent systemsDistributed computingFederated learningInformation theoryOptimizationProbabilityComputational modelingAlgorithm developmentData analysisReproducible researchExperiment tracking
Categories
Science & ResearchTechnologySoftwareData & AnalyticsEngineering
Benefits
Medical insuranceDental insuranceVision insurancePaid time offHoliday paySick time