Hongkun Yu
Senior Staff Software Engineer at Google DeepMind
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolHongkun Yu is a Senior Staff Software Engineer in the Bay Area with 11 years of experience building and leading production ML and NLP efforts at Google and DeepMind. He has driven core ML infrastructure and LLM research—contributing to Gemini, search-quality LLM integration, and TensorFlow Model Garden work—while managing teams that deliver scalable NLP models. A hands-on contributor to high-profile open-source projects like TensorFlow (TPU reference models and BERT improvements), he focuses on performance optimizations, sequence/padding handling, and export-ready model engineering. His background blends strong academic training from UIUC and international experience from Zhejiang and Simon Fraser, and he often bridges research and product by turning cutting-edge model ideas into deployable systems.
11 years of coding experience
9 years of employment as a software developer
Bachelor of Engineering (B.Eng.) Computer Science, Bachelor of Engineering (B.Eng.) Computer Science at Zhejiang UniversityThe Experimental High School Attached to Beijing Normal UniversityUniversity of Illinois Urbana-ChampaignBachelor of Science (B.Sc.) with First Class Distinction Computer Science, Bachelor of Science (B.Sc.) with First Class Distinction Computer Science at Simon Fraser University
Chinese, English, Japanese
Github Skills (9)
machine-learning10
nlp10
tensorflow10
bert10
model-optimization10
natural-language-processing10
exporter9
data-export9
exports9
Programming languages (4)
Github contributions (5)
tensorflow/models
May 2019 - Jan 2023
Models and examples built with TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:14 releases, 118 reviews, 1020 commits in 3 years 8 months
Contributions summary:Hongkun's contributions primarily focused on the development and improvement of the BERT model within the TensorFlow Models repository. They implemented various changes related to masked language modeling (MLM), sequence packing, and the handling of padding within the model, demonstrating a deep understanding of the BERT architecture and its training procedures. They also implemented the addition of a T5/MTF-style relative position bias layer. Their work involved applying best practices for performance optimization, including the removal of legacy code and improvements to the efficiency of the model's operations.
deep-learningtensorflow
tensorflow/tpu
Jan 2019 - Apr 2020
Reference models and tools for Cloud TPUs.
Role in this project:ML Engineer
ML EngineerContributions:44 commits, 33 PRs, 67 pushes in 1 year 3 months
Contributions summary:Hongkun's commits primarily involve integrating internal changes into the public repository, suggesting a focus on model updates and improvements. These changes include modifications to existing model files, specifically related to the MnasNet architecture and its associated training scripts. The user appears to be working within the TensorFlow framework, making adjustments and potentially optimizing model performance. Further, the user incorporates features and modifications into the model for efficient model export.
cloud
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