Sang Ruoxin
Senior Staff Software Engineer at Google DeepMind
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSang Ruoxin is a Senior Staff Software Engineer in Mountain View with eight years of experience building production-grade ML infrastructure and models at Google and DeepMind. He led JAX productionization efforts—shipping scalable checkpointing, model serialization, online/offline inference, quantization, and large TPU embeddings—and helped bring Gemini into production. Deep expertise in TensorFlow, TPUs, and JAX is reflected by contributions to high-profile open-source projects such as tensorflow and jax, notably optimizing ResNet implementations, XLA/multi-GPU behavior, and TPU data/sharding pipelines. Sang combines systems-level performance engineering with practical ML model work, improving reliability, error handling, and input pipelines for large-scale training and inference. Collected academic grounding from the University of Science and Technology of China underpins his applied research experience in computer vision and human expression recognition. An engineer who bridges research and production, he quietly focuses on the messy details—shape handling, control dependencies, and test coverage—that make large ML systems robust.
8 years of coding experience
10 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at University of Science and Technology of China
Github Skills (18)
distributed-training10
python10
image-classification10
machine-learning10
tpu10
keras10
deep-learning10
tensorflow10
deep-neural-networks10
resnet10
neural-network10
xla10
jax10
distributed-systems9
compiler9
Programming languages (3)
Github contributions (5)
tensorflow/models
Feb 2019 - Nov 2022
Models and examples built with TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:67 commits, 26 PRs, 37 pushes in 3 years 9 months
Contributions summary:Sang primarily contributed to the ResNet model implementation within the TensorFlow models repository, focusing on performance improvements and bug fixes related to XLA (Accelerated Linear Algebra) and multi-GPU setups. They addressed issues like static shape handling with XLA, batch size configuration, and ensuring compatibility in Keras environments. Further contributions include adding tests and flags for optional get_next_as_optional behavior in the dataset pipelines and code cleanup efforts.
deep-learningtensorflow
tensorflow/tensorflow
Apr 2018 - Jan 2023
An Open Source Machine Learning Framework for Everyone
Role in this project:ML Engineer
ML EngineerContributions:8 reviews, 245 commits, 13 PRs in 4 years 9 months
Contributions summary:Sang primarily contributed to the optimization and improvement of the TensorFlow framework, specifically within the context of TPU (Tensor Processing Unit) operations and performance. They addressed issues related to control dependencies and data sharding, enhancing efficiency by adjusting control edge handling and optimizing data transfer processes. The user also focused on error handling and logging for TPU-related functionalities.
pythondata-sciencedeep-learningmlmachine-learning
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