Yuanzhong Xu
Principal Engineer at Google DeepMind
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolYuanzhong Xu is a Principal Engineer at Google DeepMind with 12 years of experience building and scaling large-model training and serving systems, currently focused on Gemini and RL work. He led performance and parallelism efforts across flagship models (Gemini, PaLM) and core infra like GSPMD that power distributed training on TPU/GPU. His contributions span low-level compiler and sharding work in XLA/JAX/TensorFlow, improving resharding, halo exchange, and bfloat16 support to make massive, heterogeneous models practical. An active open-source contributor, he’s improved JAX’s sharding/xmap and TensorFlow/MPI-style pipelines in Lingvo, including MoE and TPU summary integrations. Trained as a PhD computer scientist, he blends deep systems and compiler expertise with hands-on ML engineering to bridge research and production-scale model training. He often surfaces subtle performance wins—reshape/propagation and compact communication patterns—that materially reduce graph size and training cost.
12 years of coding experience
7 years of employment as a software developer
PhD Computer Science, PhD Computer Science at The University of Texas at AustinBS Information Engineering, BS Information Engineering at Shanghai Jiao Tong University
Github Skills (29)
c-language10
operation10
python10
machine-learning10
sharding10
distributed-computing10
deep-learning10
tensorflow10
hla10
compiler-optimization10
xla10
compiler10
jax10
cprogramming-language10
testing9
Programming languages (2)
Github contributions (5)
openxla/xla
Dec 2017 - Dec 2022
A machine learning compiler for GPUs, CPUs, and ML accelerators
Role in this project:
Back-end Developer
Back-end DeveloperContributions:237 commits in 5 years
Contributions summary:Yuanzhong primarily contributed to improving the XLA compiler by adding and testing features related to the bfloat16 data type. Their work included adding tests for reshape and reduce-window operations, as well as implementing tuple literal conversions for bfloat16 and F32. The user also worked on propagation passes and code motion to optimize performance. The contributions centered around improving the functionality and stability of the XLA compiler.
compilercommunity-drivenmachine-learningmodular
tensorflow/tensorflow
Dec 2017 - Dec 2022
An Open Source Machine Learning Framework for Everyone
Role in this project:ML Engineer
ML EngineerContributions:315 commits, 4 comments in 5 years
Contributions summary:Yuanzhong primarily focused on optimizing and refactoring the XLA:SPMD resharding logic within the TensorFlow codebase. Their contributions included implementing compact halo exchange techniques to improve performance and reduce graph size, particularly for operations involving padding and reshaping. The user also addressed correctness issues and improved reshape propagation to better handle various sharding scenarios. They made contributions to XLA:SPMD, a system for distributed model training.
pythondata-sciencedeep-learningmlmachine-learning
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