Ziyin Huang
Senior Software Engineer at Google
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolZiyin Huang is a Senior Software Engineer at Google with seven years of experience building machine learning frameworks and infrastructure for large recommendation models and TPU-based systems. At Google they have advanced TPU embedding functionality and MLIR-level ops, and contributed to high-profile open-source projects like TensorFlow and TensorFlow Recommenders, including adding tutorials and dependency updates that improve adoption. Prior roles at Uber and Aurora focused on ML metrics for perception and production engineering, reflecting a strong bridge between research-grade ML and production systems. Trained in computational design and mechanical engineering at Carnegie Mellon and Shanghai Jiao Tong University, Ziyin brings a systems-first perspective to ML engineering, often tackling low-level performance and preprocessing optimizations that aren’t obvious from surface-level model work.
7 years of coding experience
5 years of employment as a software developer
Master of Science - MS Computational Design and Manufacturing, Master of Science - MS Computational Design and Manufacturing at Carnegie Mellon UniversityBachelor of Science - BS Mechanical Engineering, Bachelor of Science - BS Mechanical Engineering at Shanghai Jiao Tong University
English, Chinese
Stackoverflow
1reputation
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Github Skills (12)
machine-learning10
tensorflow10
python10
mlr10
tpu10
recommender-system10
deep-neural-networks9
neural-network9
deep-learning9
automation6
automations6
api-design6
Programming languages (2)
Github contributions (5)
tensorflow/recommenders
Jun 2022 - Dec 2022
TensorFlow Recommenders is a library for building recommender system models using TensorFlow.
Role in this project:
ML Engineer
ML EngineerContributions:8 commits in 6 months
Contributions summary:Ziyin primarily focused on updating and enhancing the TensorFlow Recommenders library, as evidenced by their commits. They updated the TensorFlow version dependencies across multiple files including `release.sh`, `setup.py`, `pip_install.sh`, and `test.sh`. Furthermore, they modified the `tpu_embedding_layer.py` file to include updates to the tpu embedding layer functionality. Finally, the user added a tutorial for the tpu embedding layer.
recommenderpythonrecommendersmachine-learningrecommendation-system
tensorflow/tensorflow
Mar 2022 - Jan 2023
An Open Source Machine Learning Framework for Everyone
Role in this project:ML Engineer
ML EngineerContributions:14 commits in 10 months
Contributions summary:Ziyin primarily contributed to the TensorFlow/Tensorflow repository, specifically focusing on the TPU embedding feature. Their work involved modifying the MLIR (Multi-Level Intermediate Representation) code to include TPU embedding ops, create dynamic shape copy ops, and modify their input types. They also added weight clipping and other preprocessing ops to optimize sparse core model.
pythondata-sciencedeep-learningmlmachine-learning
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