AI Research Scientist - FAIR, MSL

Meta•Menlo Park, CA

1d•$122,000 - $181,000

About The Position

Meta is seeking a Research Scientist to advance core machine learning research that powers products and systems at global scale. The role conducts original research in areas including deep learning, representation learning, generative modeling, and large-scale model training, and works with researchers and engineers to translate foundational advances into real-world impact across Meta’s AI-driven products and platforms. The position also offers the opportunity to contribute to the scientific community through publications while building systems used across Meta’s products.

Requirements

Currently has, or is in the process of obtaining, a Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience; the degree must be completed before joining Meta.
At least 2 years of experience conducting machine learning research, including developing and evaluating novel ML models or algorithms.
Experience implementing machine learning systems in Python and at least one deep learning framework such as PyTorch.
Experience designing and running experiments to validate research hypotheses, including statistical analysis of results.
Experience communicating research findings in writing, such as through technical reports, papers, or internal documentation.

Nice To Haves

Experience training or fine-tuning large-scale models on distributed compute infrastructure.
Familiarity with research areas such as self-supervised learning, reinforcement learning, or efficient model architectures.
Experience bridging research prototypes and production ML systems in a collaborative engineering environment.
Publication record at peer-reviewed machine learning venues such as NeurIPS, ICML, ICLR, CVPR, or equivalent.

Responsibilities

Design and conduct original machine learning research in areas such as deep learning, generative models, representation learning, or optimization.
Develop, implement, and evaluate novel ML algorithms and model architectures using large-scale training infrastructure.
Translate research findings into production-ready systems in collaboration with engineering and product teams.
Run controlled experiments to test research hypotheses and make data-driven decisions about model design and training strategies.
Contribute to the preparation and submission of research papers to peer-reviewed venues and conferences.
Participate in code reviews and maintain high standards for research code quality, reproducibility, and documentation.
Instrument experiments with appropriate logging, monitoring, and evaluation metrics to understand model behavior at scale.
Collaborate with cross-functional partners, including data scientists, engineers, and product teams, to identify high-impact research directions.
Identify and address performance bottlenecks in model training and inference pipelines.
Provide technical feedback to peers through design reviews, experiment critiques, and research discussions.