Zhenyu Song in

Zhenyu Song

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Applied Scientist at Amazon Web Services (AWS)
📍 Sunnyvale, California, United States

Zhenyu Song is an Applied Scientist at Amazon Web Services (AWS) AI Labs, focusing on building systems for large language models. A PhD graduate from Princeton University, his research specializes in leveraging machine learning to enhance content delivery network (CDN) caching, with a notable project deployed at YouTube.

While his public profile is professionally focused, he has demonstrated a commitment to mentoring the next generation of computer scientists, having guided several Princeton undergraduate students who have since moved on to roles at major tech companies and prestigious PhD programs.

His PhD research led to the development of a novel caching algorithm deployed in YouTubes CDN, significantly reducing infrastructure costs.

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Experience
4 Years
Current Role
Applied Scientist
Location
Sunnyvale, California, United States
Personality Overview

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Priorities

Topics Zhenyu cares about

Systems for LLMs
His current role at AWS is dedicated to developing systems for large language models, including Mixture of Experts (MoE) inference and training on specialized hardware like Trainium.
Learning-based Caching
His PhD dissertation and key publications focus on optimizing CDN caches with machine learning, directly leading to a cost-saving deployment at YouTube.
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Career

Work history

10-2023
Applied Scientist
Amazon Web Services (AWS)
7-2021 - 1-2023
Student Researcher
YouTube
5-2020 - 8-2020
Research Intern
Microsoft
3-2017 - 7-2017
Intern
ThinkForce
9-2016 - 3-2017
Research Intern
Princeton University
In the press

Media appearances

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Education
8-2017 - 8-2023
Doctor of Philosophy - PhD
Princeton University
2013 - 2017
Bachelor's degree
Shanghai Jiao Tong University
Social presence
in
Behavioral profile

DISC profile (public)

DISC behavioral profile

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