AI Resident - Learning From Videos (LFV)

• 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.

• 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. • 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.