Research Scientist, Artificial Intelligence

About the job

Meta AI Research is at the forefront of advancing foundational and applied artificial intelligence, developing breakthroughs that power products used by billions of people and shape the future of human-computer interaction. We are seeking a Research Scientist at the Staff level (IC6) with deep expertise in TPU performance optimization, large-scale model training, and systems-level machine learning. In this role, you will lead high-impact research on model efficiency and optimization for first party models within Meta's native PyTorch stack, collaborating across research and engineering teams to drive AI capabilities that define Meta's next generation of products and platforms.

Responsibilities

Lead the design and execution of TPU performance optimization research, including kernel development, memory optimization, and compute efficiency improvements Develop and optimize Pallas kernels for large-scale model training and inference on TPU architectures Drive model optimization techniques including Mixture of Experts (MoE), tensor parallelism, pipeline parallelism, and other distributed training strategies Optimize first party models within Meta's native PyTorch stack, ensuring efficient integration with XLA compilation and TPU execution Identify and resolve complex technical challenges in model training efficiency, inference latency, and system reliability that require novel approaches Define and drive multi-quarter research roadmaps for TPU optimization, aligning project milestones with broader organizational goals Establish rigorous experimentation frameworks for performance benchmarking, including metric selection, profiling methodology, and data-driven optimization decisions Translate research findings into production-ready optimizations by collaborating with engineering teams on deployment pipelines and reliability at scale Communicate research findings and technical trade-offs clearly through publications, design documents, and presentations to both technical and non-technical audiences Mentor other researchers and engineers on TPU optimization techniques, providing structured feedback on technical direction and experimental rigor