AI Resident - Learning From Videos (LFV)

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.

Qualifications

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.

Bonus Qualifications

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.