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.
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 who thrives at the intersection of machine learning and materials science and is motivated to develop the next generation of material representations—integrating signals from multiple characterizations and measured/computed properties to support accelerated materials design. In this role, you’ll collaborate with TRI researchers to prototype, evaluate, and ship representation-learning approaches that can serve as a foundation for forward property prediction and inverse design workflows.
This is a summer 2026 paid 12-week internship opportunity. Please note that this internship will be an in-office role.
Responsibilities
Build and maintain strong baselines comparing composition-only and structure-aware representations across a set of key property prediction tasks.
Prototype and evaluate multi-view representation methods that integrate signals from multiple characterizations and properties.
Develop a reusable evaluation and reporting pipeline to assess generalization, identify failure modes, and quantify uncertainty where appropriate.
Curate datasets and implement preprocessing workflows that better capture complex materials systems and common sources of noise or ambiguity.
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 master's or doctoral program in materials science, computer science, statistics, applied math, machine learning, or a related discipline.
Strong foundations in machine learning with demonstrated experience training models on real datasets.
Familiarity with modern scientific ML approaches, including representation learning, uncertainty estimation, and/or physics-informed or hybrid physics–ML modeling.
Proficiency in Python and modern ML tooling (e.g., PyTorch/JAX, experiment tracking, reproducibility best practices).
Ability to work collaboratively across disciplines and to translate research ideas into working code.
Please add a link to Google Scholar to include a full list of publications when submitting your CV for 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. Base pay offered will depend on multiple individualized factors, including, but not limited to, business or organizational needs, market location, job-related knowledge, skills, and experience. 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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