Kexin Yu - Software Engineer at Google

Kexin Yu

Software Engineer at Google

San Francisco Bay Area United States

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Summary

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Rockstar

🎓

Top School

Kexin Yu is a software engineer with 10 years of experience building scalable machine learning and large-scale data systems across industry leaders in the Bay Area, currently at Google after a deep-learning frameworks role at NVIDIA. She holds an MS from Stanford ICME and a BS in Mathematics of Computation from UCLA, blending strong theoretical foundations with production engineering. Her open-source contributions include significant optimizer work on NVIDIA/apex—improving mixed-precision and distributed training primitives like FusedSGD and FusedLAMB—demonstrating both low-level CUDA-aware optimization and practical ML tooling. Past roles span research and applied engineering at Baidu USA and Verizon, where she delivered GPU-accelerated algorithms, adaptive sampling strategies, and automated model-selection systems. Colleagues describe her as someone who bridges research and production: she moves models from prototype to robust, deployable components. Outside core development she’s reviewed career-track submissions for the Grace Hopper Conference, reflecting a commitment to community and mentorship.

10 years of coding experience

3 years of employment as a software developer

Qingdao No.2 Middle School

Bachelor's Degree, Mathematics of Computation, Bachelor's Degree, Mathematics of Computation at University of California, Los Angeles

Master of Science - MS, Computational and Mathematical Engineering, Master of Science - MS, Computational and Mathematical Engineering at Stanford University

Chinese, English

Github Skills (6)

multiprecision10

pytorch10

distributed-training10

apex10

optimization10

cuda9

Programming languages (2)

C++Python

Github contributions (5)

NVIDIA/apex

Dec 2019 - Sep 2021

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

Role in this project:

ML Engineer

Contributions:3 reviews, 98 commits, 22 PRs in 1 year 8 months

Contributions summary:Kexin primarily contributed to optimizing and extending the `apex.contrib.optimizers` module, a PyTorch extension for mixed precision and distributed training. Their work involved significant modifications to the `FusedSGD` and `FusedLAMB` optimizers, incorporating features like global gradient clipping, and addressing state-dict mismatches. Further contributions included the integration of multi-tensor L2 norm calculation and debugging/testing. They also made relevant edits to example files and build scripts.

pytorchraymixed-precisiondeep-learningtemporal-data

yws12/CS341_cryptocurrency_prediction

Apr 2018 - Jun 2018

Contributions:73 commits, 67 pushes in 1 month

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