Research Scientist - Reality Labs

Overview

Meta Reality Labs is seeking a Research Scientist with deep expertise in large language models to advance AI-powered neuromotor interactions for wearable devices. The role focuses on next-generation language capabilities for Meta’s wearable platforms, including work connected to the company’s electromyography (EMG) program. The team sits within Wearables Input and Interaction and combines machine learning, language modeling, and on-device optimization for constrained hardware.

What You'll Do

• Lead the design, development, and optimization of large language models for wearable device applications.
• Set technical direction for LLM research efforts involving a small team of researchers and engineers.
• Run research and experiments to improve model accuracy, efficiency, and on-device performance.
• Work with engineering, HCI, and product partners to move LLM research into production.
• Explore model optimization approaches for resource-constrained environments, including quantization and other efficiency techniques.
• Stay current with advances in LLMs, NLP, and related research areas.

Requirements

• Bachelor’s degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience.
• Currently has, or is in the process of obtaining, a PhD in Computer Science, Machine Learning, Natural Language Processing, or a related field; the degree must be completed before joining Meta.
• Demonstrated expertise in large language models, including architecture design, training, fine-tuning, and/or deployment.
• Programming experience in Python and hands-on experience with deep learning frameworks such as PyTorch.
• Experience developing machine learning models at scale from inception through impact.
• 5+ years of research experience working independently on ML/NLP problems.

Preferred Qualifications

• Experience designing knowledge distillation pipelines to compress frontier LLMs into optimized edge models.
• Experience bringing LLM-based products from research into production.
• Software engineering experience through internships, work experience, or widely used open source contributions.
• Technical leadership experience setting direction for a small research/engineering team.
• First-authored publications at peer-reviewed AI/NLP conferences such as ACL, EMNLP, NeurIPS, ICML, ICLR, or NAACL.
• Experience with on-device or edge language model optimization, including quantization, sparsity, and distillation.
• Experience with logit matching, task-specific SFT data synthesis, and instruction tuning for production sub-billion models.