Research Scientist, Driver Impairment Detection and Intervention

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 in Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavioral Models, and Robotics.

The Human Aware Interactions and Learning team uses approaches from machine learning, robotics, and computer vision, along with insights from human factors literature, to devise new techniques that improve on the state of the art towards better machine understanding, prediction, and interactions with people in the driving domain, both in and around the vehicle. We work with computational and cognitive researchers to test our approaches from a variety of data sources and human-in-the-loop experiments to devise ML approaches that work with the driver.

We are seeking a Research Scientist to lead groundbreaking research at the intersection of machine learning, computer vision, and human factors. This role focuses on understanding, detecting, and developing intervention strategies for driver impairments, such as cognitive distraction and intoxication. The ideal candidate will contribute to fundamental research, publish in top-tier venues, and build machine learning models and prototypes that integrate human-in-the-loop data towards novel approaches for understanding and assisting drivers under diverse situations.

This is an opportunity to work on innovative research in human-robot interaction and intelligent vehicle systems in a collaborative and interdisciplinary team of experts in robotics, AI, and human factors. You will have access to innovative robotic platforms and simulation tools with the potential to contribute to academic publications and impactful real-world applications.

Responsibilities

• Conduct original research on driver impairment detection and intervention (e.g. warning, coaching, actuation) using machine learning and computer vision.
• Develop algorithms and models to analyze driver behavior, physiological signals, and other multimodal inputs.
• Design, implement, and conduct human-in-the-loop behavioral studies, ensuring robustness and real-world applicability.
• Publish findings in high-impact conferences and journals.
• Collaborate with interdisciplinary teams, including human factors experts, cognitive scientists, and engineers.
• Prototype and validate ML-based intervention strategies to enhance driver safety and performance.

Qualifications

• PhD in Computer Vision, Machine Learning, Human-Centered AI, or a related field.
• Research experience in human and machine vision, behavior analysis, or multimodal learning.
• Strong publication record (e.g., CVPR, NeurIPS, ICCV, ICLR).
• Experience working with human-in-the-loop data: data collection, annotation strategies, and model training.
• Proficiency in deep learning frameworks (e.g., PyTorch, Jax, Hugginface) and data analysis tools.
• Ability to work both independently and as part of an interdisciplinary team.

Bonus Qualifications

• Experience in developing real-time AI systems for human monitoring.
• Familiarity with physiological and cognitive state estimation (e.g., eye tracking, EEG, heart rate variability).
• Background in human factors, cognitive psychology, or related fields.
• Experience deploying machine learning models in real-world environments.
• Knowledge of software development industry practices (version control, CI/CD, documentation).

Please submit a brief cover letter and add a link to Google Scholar to include a full list of publications when submitting your CV for this position.
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