The PyTorch TPU team focuses on enabling efficient and accurate inference of 1P models directly on TPU. The work spans performance modeling, sharding and parallelism design, kernel authoring and automating with agents.
Our internships are twelve (12) to twenty four (24) weeks long, and we have various start dates throughout the year.
Develop, evaluate methodologies and systems for running LLM/Multi-modality models cost-effectively on TPU systems.
Currently has, or is in the process of obtaining, a PhD in computer science, computer engineering, electrical engineering, or a related field
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