Toyota Research Institute
Accelerated Science Intern, Multi-Agent Learning and Coordination
Los Altos, CA · On-site · Posted 2026-11-05
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.
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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