Accelerated Science Intern, Multi-Agent Learning and Coordination

Toyota Research Institute•Los Altos, CA

2d•Hybrid

About The Position

Toyota Research Institute (TRI) is hiring a research intern for its Accelerated Materials Design and Discovery (AMDD) program. The program aims to accelerate the development of emissions-free mobility by combining computational materials modeling, experimental data, artificial intelligence, and automation to advance materials research. AMDD collaborates with universities, national laboratories, and colleagues across global Toyota, and works to translate emerging technologies into practice within Toyota and the broader open research community. The intern will develop algorithms and computational systems for multi-agent learning and coordination. The research focuses on how agents with different data and capabilities can share information, build predictive models, and choose computational tasks to improve their collective understanding of data. Potential approaches include distributed and federated learning, along with information-theoretic methods for communication and task selection. The intern will implement and benchmark these methods, evaluating effects on model accuracy, communication costs, and computational efficiency. This is a paid, 12-week internship and a hybrid, in-office role. The expected California-based pay range at commencement is $45–$65 per hour. TRI offers medical, dental, and vision insurance, as well as paid time off benefits including holiday pay and sick time.

Requirements

Currently enrolled in a doctoral program in computer science, machine learning, statistics, applied mathematics, 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 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

Experience with distributed computing.

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