Meta's mission is to give people the power to build community and bring the world closer together, and the Meta Reality Labs organization focuses on delivering this vision through AR/VR/SG devices.
Meta's Efficiency and On-Device AI team within the Reality Labs organization is seeking a Research Scientist Intern with a focus on language/vision model optimization. As a Research Scientist Intern, your primary objective is to develop cutting-edge efficient LLMs and vision foundation models.
Responsibilities
- Perform LLM and vision foundation model research that leads to publications in top-tier CV/NLP/ML conferences.
- Develop model optimization algorithms and infrastructure to enable deployment of models in resource-constrained settings, such as mobile phones and AR/VR/MR/SG devices.
- Research joint optimization of models and the surrounding assistant system, including planning, memory, tool use, and coordination across device and cloud.
- Explore personalization, model compression and efficiency, and novel model architectures including omni-modal capabilities.
- Collaborate with cross-disciplinary research and engineering teams.
Qualifications
- Currently is in the process of obtaining a PhD in the field of Artificial Intelligence or related field
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment
- Hands-on experience in deep learning algorithms and techniques, e.g., transformers
- Experience with deep learning software libraries such as PyTorch/JAX
- Experience with scripting languages such as Python and shell scripts Intent to return to degree-program after the completion of the internship
- Experience with large scale foundation model training and on-device foundation model optimization
- Experience in one or more of: LLM agents and orchestration, personalization, model compression and efficient inference, efficient model architectures, multi-modal modeling, data-centric approaches for model tuning and evaluation
- Proven track record of publications at CV/NLP/ML conferences/journals such as NeurIPS, ICLR, ICML, CVPR, ICCV, ECCV, AAAI, ACL, EMNLP