Research Scientist - Reality Labs

Meta; Sunnyvale (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Reality Labs at Meta is seeking a Research Scientist with deep expertise in Large Language Models to advance AI-powered neuromotor interactions for wearable devices. The role sits within Wearables Input and Interaction and blends ML, language modeling, and on-device optimization to push language AI on constrained hardware.

The ideal candidate has 5+ years of autonomous ML/NLP research, experience distilling knowledge to edge models, and a track record of bringing LLM products from research to

Qualifications

• Bachelor's degree or equivalent; PhD completed prior to joining Meta.
• Expertise in Large Language Models: architecture design, training, fine-tuning, deployment.
• Proficient in Python and deep learning frameworks such as PyTorch.
• Experience scaling ML models from inception to impact.
• 5+ years of autonomous ML/NLP research experience.

Responsibilities

• Drive innovation in language model development, optimization, and deployment.
• Set technical direction for a small team of 3-4 researchers/engineers.
• Translate research into real-world products for wearables with EMG tech.

Skills

LLM design

Machine Learning

NLP

Python

PyTorch

Knowledge distillation

Edge model optimization

On-device ML

Technical leadership

Research publications

Production deployment

Open source contributions

Education

Bachelor’s degree in CS/CE or related field

PhD in CS/ML/NLP or related field

Tools

Python

PyTorch

Job description

Reality Labs at Meta is seeking a Research Scientist with deep expertise in Large Language Models (LLMs) to advance our work in AI-powered neuromotor interactions for wearable devices. We are building next-generation language capabilities that enable natural, expressive, and intelligent communication through Meta's wearable platforms via our industry-leading electromyography (EMG) program.

Our team within the Wearables Input and Interaction organization is multi-disciplinary, combining machine learning, language modeling, and on-device optimization to push the boundaries of what's possible with language AI on constrained hardware. This is a unique opportunity to conduct cutting-edge LLM research with direct product impact — shaping how people interact with the next generation of wearable devices.

We are looking for a researcher who can drive innovation in language model development, optimization, and deployment while setting technical direction for a small team. If you are passionate about building state-of-the-art language models and translating research into real-world products, we want to hear from you.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experienceCurrently has, or is in the process of obtaining, a PhD in Computer Science, Machine Learning, Natural Language Processing, or a related technical field. Degree must be completed prior to 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 to impact
• 5+ years of research experience working autonomously on ML/NLP problems
• Proven track record of architecting knowledge distillation (KD) pipelines to compress frontier LLMs into optimized edge models
• Experience bringing LLM-based products from research to production
• Demonstrated software engineering experience via internship, work experience, or widely used contributions in open source repositories
• Technical leadership experience setting direction for a team of 3-4 researchers/engineers
• First-authored publications at peer-reviewed AI/NLP conferences (e.g., ACL, EMNLP, NeurIPS, ICML, ICLR, NAACL)
• Experience with on-device or edge language model optimization (quantization, sparsity, distillation, knowledge distillation)
• Deep expertise in logit matching, task-specific SFT data synthesis, and instruction-tuning sub‑billion models for production tasks