AI Research Scientist, Multimodal Perception

Job description

Meta is seeking Research Scientists to join Meta Reality Labs organization to work on making significant advances in vision and multi-modal perception. You will work on vision models, image and video understanding and sensor fusion. The team builds efficient on-device models that will be the foundation for a new paradigm in how we perceive the world. You will work with a highly interdisciplinary team of scientists, engineers, and cross-functional partners with a broad range of experiences, perspectives, approaches, and backgrounds and access cutting-edge technology, resources, and research facilities.

Responsibilities

• Build models that can understand images and videos
• Conduct research and work with efficient vision encoders, multi-modal LLMs
• Define, build and benchmark new capabilities needed for the next generation of AI Train and optimize state-of-the-art machine learning and neural network methodologies
• Work with and create large datasets, and contribute to data annotation engines

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience AI Research Scientist, multimodal perception (PhD) Research Experience in deep learning, computer vision, robotics, or AI infrastructure Experience with Python and PyTorch Experience developing software and executing complex experiments Experience communicating research for public audiences of peers
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward
• Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g.

GitHub)

• Research experience with efficiency as a focus Experience working and communicating cross functionally in a team environment
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences such as CVPR, ICCV, ECCV, ICML, NeurIPS or similar