AI Research Scientist - FAIR, MSL

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

Minimum Qualifications

• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
• 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 written form, such as technical reports, papers, or internal documentation

Preferred Qualifications

• 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

Compensation

• $122,000/year - $181,000/year; Country: US; Bonus eligible; Equity eligible

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