Accelerated Science Intern, Representation Learning for Molecules and Materials

Toyota Research Institute•Los Altos, CA

8d•Hybrid

About The Position

Toyota Research Institute (TRI) is offering a paid 12-week internship in its Accelerated Materials Design and Discovery (AMDD) program. AMDD aims to accelerate the development of emissions-free mobility by discovering materials and devices for batteries, fuel cells, and other applications. The program combines computational materials modeling, experimental data, artificial intelligence, and automation, and collaborates with universities, national laboratories, and colleagues across Toyota. The intern will work with TRI researchers to investigate how machine learning models represent molecules and materials, and whether different models learn the same underlying physical structure. The work will examine how training data, learning objectives, and model architectures shape learned representations, using mathematical reasoning and computational experiments. The position is hybrid and in-office.

Requirements

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.
Demonstrated experience training models on real datasets.
Proficiency in Python and modern machine-learning tooling, such as PyTorch, experiment tracking, and reproducibility best practices.
Ability to work collaboratively across disciplines and translate research ideas into working code.

Nice To Haves

Familiarity with representation learning or scientific machine learning.
Experience studying model convergence, learning dynamics, symmetries, or identifiability.

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.
Help translate results into internal reports, publications, and/or patent disclosures, as appropriate.