AI Resident - Learning From Videos (LFV)

At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. The Learning From Videos (LFV) team in the Robotics division focuses on the development of foundation models capable of leveraging large-scale multi-modal data from multiple domains. This year-long AI Residency is a research-focused position designed for early-career researchers and engineers who are excited to work on ambitious problems in embodied AI. The resident will collaborate closely with researchers and engineers at TRI on high-risk, pushing forward our understanding of spatio-temporal reasoning and zero-shot generalization.

Responsibilities

Develop, integrate, and deploy algorithms for Multi-Modal and 4D reasoning targeting physical applications.; Handle the ingestion of large-scale datasets for training, including streaming, online, and continual learning.; Contribute innovative solutions at the intersection of machine learning, computer vision, and robotics to improve real-world task performance.; Work closely with robotics and machine learning researchers and engineers to understand theoretical and practical needs.; Follow best practices producing maintainable code, both for internal use as well as for open-sourcing to the scientific community.; Contribute to research publications and technical reports.

Requirements

Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related technical field; Exceptional candidates with equivalent research experience (e.g., strong publication record, open-source contributions, or industry research experience) are encouraged to apply; Strong background in computer vision and its applications to robotics and embodied systems; Demonstrated research experience through publications, technical projects, or open-source contributions; Strong communication skills and a collaborative mindset, with the ability to learn quickly and contribute to team research efforts; Passionate about assisting and amplifying older adults and those in need through dexterous manipulation, human-robot collaboration, and physical assistance innovation.

Nice to have

Spatio-temporal (4D) computer vision, including multi-view geometry, 3D/4D reconstruction, video generation, self-supervised learning, occlusion reasoning, etc.; Large-scale training of multi-modal deep learning methods, both in terms of dataset sizes and model complexity, context length extension, and efficient attention, distributed computing, etc.; Application of machine learning and computer vision to embodied applications.

Benefits

medical, dental, and vision insurance; paid time off benefits (including holiday pay and sick time)