Research Engineer, Fundamental AI Research (FAIR) - Generative Models/LLM Acceleration.

Meta's Fundamental AI Research (FAIR) organization is seeking a Research Engineer to drive advancements in generative models. The role involves working across the full spectrum of research, engineering, and optimization for frontier model efforts.

• Innovate, lead, and execute pioneering algorithmic research to push the state-of-the-art in generative models and LLM performance
• Directly contribute to the experimental process, including designing details, implementing reusable code, running evaluations, and organizing results
• Collaborate with cross-functional teams (research, product, infra) to build new and advance LLM optimization
• Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews
• Lead initiatives, provide technical guidance and mentorship to peers, and help onboard new team members
• Take a significant role in components, features, or systems with good end-to-end understanding
• Contribute to publications, open-sourcing initiatives, and mentor other team members

• Master's degree or higher in a relevant technical field (e.g., Computer Science, Machine Learning, AI, or related discipline)
• 6+ years of experience in machine learning, deep learning, or AI research, or equivalent practical experience
• Experience designing and implementing large-scale model training pipelines using frameworks such as PyTorch or JAX
• Experience with distributed computing and parallel training techniques including data parallelism, model parallelism, or pipeline parallelism
• Experience debugging and optimizing AI systems for performance, reliability, and correctness across the full model lifecycle

• Experience building evaluation frameworks and benchmarking pipelines to measure model quality and capability regressions
• Experience with large language model pretraining, fine-tuning, post-training, or inference optimization
• Track record of contributions to peer-reviewed AI research publications or open-source AI frameworks