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Research Scientist, Artificial Intelligence

Meta· Research
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Menlo Park, CA$184k - $257k + bonusFull-timePosted 3w ago

About the role

Meta AI Research seeking a Staff-level Research Scientist with expertise in TPU performance optimization, large-scale model training, and systems-level machine learning to lead high-impact research on model efficiency and optimization within Meta's native PyTorch stack.

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

Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 8+ years of experience in machine learning systems, model optimization, or high-performance computing research
  • Experience with TPU architecture and performance optimization, including profiling, kernel development, and memory management
  • Experience with XLA compilation, graph optimization, and low-level performance tuning for accelerator hardware
  • Experience developing and optimizing large-scale distributed training systems, including parallelism strategies such as data, tensor, and pipeline parallelism
  • Experience with PyTorch and its integration with accelerator backends
  • Experience communicating complex technical findings in writing, including technical reports, design documents, or peer-reviewed publications

Skills

Required:TPU architecture and performance optimizationXLA compilationLarge-scale distributed trainingPyTorch integration with accelerator backends
Nice to have:MoE optimization

Benefits

  • benefits information referenced in posting (external link)
Last updated Oct 23, 2026. Interested? Apply on the company site.

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