Research Scientist Intern, TPU(PhD)

Meta · Advertising · Virtual & Augmented Reality · Doctoral Degree (PhD) · Python · 5 hours ago
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In office (Menlo Park)
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Intern
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Confirmed on the employer's own hiring board on Nov 12, 2026. First seen by Alion on Nov 11, 2026. Meta scores B on the Alion truth index.

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Meta

Meta Platforms is an American technology company founded in 2004 as Facebook and renamed in 2021 to signal its investment in immersive computing. It operates the largest family of social and messaging applications in the world, comprising Facebook, Instagram, WhatsApp, Messenger and Threads, and earns almost all of its revenue from advertising sold against that audience. Headquartered in Menlo Park, California, the company also develops the open Llama language models and the Reality Labs division behind Quest headsets and Ray-Ban smart glasses.
meta.com · HQ: Menlo Park, United States · Foundation Models · Advertising · Virtual & Augmented Reality · Messaging Apps · Social Networks · 5000+ employees · Est. 2004
Tech stack of Meta
Backed by SV Angel, ValueAdd VC, 7percent Ventures
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.

Responsibilities

Develop, evaluate methodologies and systems for running LLM/Multi-modality models cost-effectively on TPU systems. Analyze and extract insights from traffic pattern to derive computational intrinsics that can be leveraged into actions in system and kernel optimization.

Qualifications

Currently has, or is in the process of obtaining, a PhD in computer science, computer engineering, electrical engineering, or a related field Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment Proficient in Python Experience with system and kernel optimization Experience with GPU/TPU/ASICs Experience with Triton, Cuda, Cutedsl, Pallas Familiarity with performance modeling

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