Yeounoh Chung
Senior Software Engineer at Google
San Francisco Bay Area United States
Summary
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Rockstar🎓
Top SchoolYeounoh Chung is a Senior Software Engineer with 11 years of experience, currently building production-grade ML and infrastructure at Google in the San Francisco Bay Area. He specializes in MLOps and DevOps work that bridges PyTorch with XLA hardware, contributing to high-profile open-source projects like pytorch/pytorch and pytorch/xla to enable TPU and other XLA backends. His contributions include enabling float16 autocasting, refactoring DTensor XLA APIs, improving model sharding efficiency, and hardening CI/CD for ML tests—work that improves both developer workflows and runtime performance. Yeounoh combines deep academic training (BS and MEng from Cornell; PhD studies at Brown) with hands-on engineering to move research-grade tooling into reliable production. He’s known for pragmatic refactors that unlock new hardware capabilities rather than surface-level features, and for smoothing complex integrations between PyTorch and TensorFlow/XLA ecosystems.
11 years of coding experience
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Brown UniversityBachelor of Science - BS, Bachelor of Science - BS at Cornell University
Stackoverflow
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Github Skills (19)
pytorch10
python10
machine-learning10
distributed-computing10
deep-learning10
tensorflow10
ci-cd-pipeline10
build-automation10
xla10
devops10
dockers9
docker9
gpu9
compiler-compiler9
tensor9
Programming languages (5)
Github contributions (5)
pytorch/xla
Nov 2021 - Jan 2023
Enabling PyTorch on XLA Devices (e.g. Google TPU)
Role in this project:
DevOps Engineer & ML Engineer
DevOps Engineer & ML EngineerContributions:917 reviews, 209 commits, 352 PRs in 1 year 2 months
Contributions summary:Yeounoh's contributions primarily focused on improving the CI/CD pipeline and enabling testing for machine learning models within the `pytorch/xla` repository. They made changes to the CircleCI configuration, including switching base images, installing dependencies, and enabling tests for various machine learning optimizer configurations. Additionally, the user addressed build issues and updated dependencies related to the TensorFlow integration, essential for the project's goal of enabling PyTorch on XLA devices. They also made code adjustments to enhance the efficiency of model sharding.
pytorchxladeep-learningtpucompiler
pytorch/pytorch
Feb 2022 - Dec 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:MLOps Engineer
MLOps EngineerContributions:34 reviews, 22 commits, 31 PRs in 10 months
Contributions summary:Yeounoh primarily contributed to the PyTorch/XLA integration, working on features to support XLA backend for the DTensor API. Their work included enabling float16 dtype for XLA autocasting and refactoring the DTensor XLA API. The user also supported XLA backend in the distribute_module API and fixed an issue with the fused path in Linear for the XLA backend. These contributions suggest a focus on enabling and optimizing PyTorch's functionality on XLA-based hardware.
pythongpu-accelerationdeep-learninggpunumpy
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