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

**Summary:** 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. **Required Skills:** Research Engineer, Fundamental AI Research (FAIR) - Generative Models/LLM Acceleration Responsibilities: 1. Innovate, lead, and execute pioneering algorithmic research to push the state-of-the-art in generative models and LLM performance 2. Directly contribute to the experimental process, including designing details, implementing reusable code, running evaluations, and organizing results 3. Collaborate with cross-functional teams (research, product, infra) to build new and advance LLM optimization 4. Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews 5. Lead initiatives, provide technical guidance and mentorship to peers, and help onboard new team members 6. Take a significant role in components, features, or systems with good end-to-end understanding 7. Contribute to publications, open-sourcing initiatives, and mentor other team members **Minimum Qualifications:** Minimum Qualifications: 8. Master's degree or higher in a relevant technical field (e.g., Computer Science, Machine Learning, AI, or related discipline) 9. 6+ years of experience in machine learning, deep learning, or AI research, or equivalent practical experience 10. Experience designing and implementing large-scale model training pipelines using frameworks such as PyTorch or JAX 11. Experience with distributed computing and parallel training techniques including data parallelism, model parallelism, or pipeline parallelism 12. Experience debugging and optimizing AI systems for performance, reliability, and correctness across the full model lifecycle **Preferred Qualifications:** Preferred Qualifications: 13. Experience building evaluation frameworks and benchmarking pipelines to measure model quality and capability regressions 14. Experience with large language model pretraining, fine-tuning, post-training, or inference optimization 15. Track record of contributions to peer-reviewed AI research publications or open-source AI frameworks **Industry:** Internet