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Research Scientist Intern, PyTorch Compiler

United States, Menlo Park 7650.00 - 12134.00 USD / Month · Job Posted January 29, 2026
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Job Description

Our team makes PyTorch run faster and more resource-efficient without sacrificing the flexibility and ease of use of PyTorch. Team scope: - Advance PyTorch 2.0 technologies that bring torch.compile() to the heart of PyTorch - Advance PyTorch Distributed through torch.compile() - Improve PyTorch out-of-the-box performance on GPU, CPU, accelerators - Vertical performance optimization for models for training and inference Our team at Meta AI offers twelve (12) to sixteen (24) weeks long internships and we have various start dates throughout the year.

Job Responsibility

  • Develop new techniques in TorchDynamo, TorchInductor, PyTorch core, PyTorch Distributed
  • Explore the intersection of PyTorch compiler and PyTorch Distributed
  • Optimize Generative AI models across the stack (pre-training, fine-tuning, and inference)
  • Improve general PyTorch performance
  • Conduct cutting-edge research on ML compiler and ML distributed technologies
  • Collaborate with users of PyTorch to enable new use cases for the framework both inside and outside Meta

Requirements

  • Currently has or is in the process of obtaining a PhD degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • Experience in ML compiler, Distributed Training, ML systems, or similar
  • Proficient in Python or Cuda programming
  • Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Nice to have

  • Experience working on other ML compiler stack, especially on PT2 stack or Triton
  • Experience doing performance optimization on machine learning models
  • Intent to return to degree program after the completion of the internship/co-op
  • Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, MLSys, ASPLOS, PLDI, CGO, PACT, ICML, or similar
  • Experience working and communicating cross functionally in a team environment
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