Research Scientist Intern
Meta
This job at a glance
- Who can apply
- New grads and up to 1 year
- Must know
- Data structures & algorithmsDeep learningPyTorchPython
- Nice to have
- LLMsMachine learningNLP
- Work
- On-site in Burlingame · internship
- Pay
- Not stated by the employer.
- Skill demand
- Python32%Data structures & algorithms9%Deep learning9%PyTorch9%of 99 similar entry-level jobs posted in the US this week
- Listed
- Oct 28, 2026 · still open
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About the Role
Meta's Reality Labs organization, specifically the Efficiency and On-Device AI team, is seeking a Research Scientist Intern with a focus on language/vision model optimization. The primary objective is to develop cutting-edge efficient LLMs and vision foundation models.
Key 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.
Requirements & Tech Stack
- 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.
Qualifications
- Currently 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.
- Intent to return to degree-program after the completion of the internship (Preferred).
- Experience with large scale foundation model training and on-device foundation model optimization (Preferred).
- 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 (Preferred).
- Proven track record of publications at CV/NLP/ML conferences/journals such as NeurIPS, ICLR, ICML, CVPR, ICCV, ECCV, AAAI, ACL, EMNLP (Preferred).