--- title: 'Accelerated Science Intern, Representation Learning for Molecules and Materials at Toyota Research Institute' canonical: 'https://feeny.ai/job/accelerated-science-intern-representation-learning-for-molecules-and-materials-jphdb28vqmsv' type: 'job' last_seen: '2026-11-02' --- # Accelerated Science Intern, Representation Learning for Molecules and Materials at Toyota Research Institute - **Company:** Toyota Research Institute - **Location:** Los Altos, CA - **Employment:** internship - **Work type:** hybrid - **Posted:** 2026-10-30 - **Last confirmed live:** 2026-11-02 - **Apply:** https://jobs.lever.co/tri/9c851b9d-169d-4bb3-b164-828098e077e7 ## Job 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 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. ## 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. 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. ## About Toyota Research Institute ## Company Overview - **One-liner**: Toyota Research Institute (TRI) conducts advanced research in AI, robotics, automated driving, and energy materials to amplify human ability and improve quality of life. - **Entity Type**: Private (Subsidiary of Toyota Motor Corporation) - **Headquarters**: Los Altos, California, United States - **Founded**: 2016 (per LinkedIn) - **Founders**: Not publicly disclosed; led by CEO Dr. Gill Pratt ## Core Business - **Primary industries**: Artificial intelligence, robotics, automated driving, energy & materials, human-centered AI - **Target customers**: Internal (Toyota), academic partners (Stanford, MIT), and eventual commercial deployment through Toyota - **Mission**: “Improve the quality of human life” and “Enable mobility for all” by creating tools and capabilities that make lives safer, more sustainable, and more independent. ## Products & Services TRI is organized around four core research areas, each producing prototypes, software, and intellectual property: - **Automated Driving**: Developing safe, scalable automated driving technology to enable mobility for all. - **Energy & Materials**: Accelerating carbon neutrality through innovations in batteries, hydrogen fuel cells, and emissions-free vehicles. - **Human-Centered AI**: Using AI to understand human behavior and interactions, aiding decision-making and improving human-machine collaboration. - **Human Interactive Driving**: Building intelligent vehicles that place the human at the center of the driving experience. - **Robotics**: Creating human‑amplification robotic capabilities for aging society, labor shortages, and sustainable production (mobile manipulation, user interfaces, prototyping). ## Market Standing - **Valuation / Market Cap**: Not applicable (private subsidiary of Toyota Motor Corporation) - **Key Metric**: Headcount of 323 employees (+12.6% YoY, +53 people) as of late 2025/early 2026 - **Notable Investors / Partners**: Toyota Motor Corporation (parent), Stanford University, Massachusetts Institute of Technology (MIT), Toyota Ventures (venture arm) - **Growth Signals**: 12.6% headcount growth; 978 scholarly works with 141,269 citations (field‑weighted citation impact 4.9); 46% of works involve international collaboration; active hiring across multiple research and engineering roles ## Competitive Advantages - **Toyota Backing**: Access to Toyota’s global resources, manufacturing expertise, and long‑term strategic vision. - **Top Talent**: Over half of the technical team holds PhD degrees; 454 actively affiliated researchers, with 266 recent arrivals. - **Academic Partnerships**: Close collaboration with Stanford and MIT, enabling cutting‑edge research and talent pipeline. - **Mission‑Driven Research**: Focus on real‑world impact (mobility, aging society, carbon neutrality) rather than short‑term product cycles. ## Strategic Focus - **Current Priorities**: Advancing embodied AI and large behavior models; scaling mobile manipulation robots for real‑world tasks; developing next‑generation battery and hydrogen fuel cell materials; achieving emissions‑free mobility. - **Direction for Growth**: Expanding robotics capabilities to address labor shortages and aging populations; deepening human‑AI interaction research; bringing advanced mobility technology to market faster through Toyota. ## Why Work Here - **Work‑Life Balance**: 4‑day workweek (every Friday off), flexible hybrid model, 4 weeks paid vacation, generous holiday schedule. - **Benefits**: 401(k) with matching and profit sharing, medical/dental/vision, 16‑26 weeks paid parental leave, back‑up child/elder care, gender‑affirming care, fertility benefits, pet insurance, life insurance. - **Wellness & Development**: Monthly well‑being stipend, on‑demand fitness/nutrition/mindfulness resources, employee assistance program, learning and development programs. - **Culture**: Diverse and inclusive community; values‑driven collaboration; “how we work” is as important as what we do. - **Locations**: Los Altos (HQ, Silicon Valley) and Cambridge, MA (robotics lab). Remote/hybrid options available. - **Engineering Culture**: Strong emphasis on research excellence, PhD‑heavy environment, opportunity to publish and patent, intern program for top students. ## Sources 1. [tri.global/careers](https://www.tri.global/careers) 2. [tri.global/about-us](https://www.tri.global/about-us) 3. [linkedin.com/company/toyota-research-institute](https://linkedin.com/company/toyota-research-institute) 4. [jobs.lever.co/tri](https://jobs.lever.co/tri) 5. 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