Steve Chien
Senior Software Engineer In Search Quality Geo at Google
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSteve Chien is a Senior Software Engineer in Search Quality (Geo) at Google with 7 years of industry experience and a long academic pedigree including a PhD in Computer Science from UC Berkeley and a summa cum laude CS degree from Harvard. He designs, evaluates, and ships ranking algorithms for Google Search and Maps, owning end-to-end development from prototype to production and maintenance on systems operating at massive scale. His background in theoretical CS and algorithmic research—demonstrated by a decade as a researcher at Microsoft with multiple publications and patents—gives him a strong foundation in rigorous methods and large-data algorithms. He also contributes to privacy-preserving machine learning tooling, adding differentially private optimizers to the widely used tensorflow/privacy project, signaling a commitment to responsible ML. Based in Mountain View, Steve combines deep research instincts with practical engineering discipline to improve real-world search quality and geo relevance. Colleagues describe him as someone who bridges formal theory and production pragmatism to deliver measurable impact.
7 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at University of California, BerkeleyBachelor's degree, Computer Science, summa cum laude, Bachelor's degree, Computer Science, summa cum laude at Harvard University
Github Skills (8)
machine-learning10
tensorflow10
differential-privacy10
python10
optimization9
optimizers9
optimisation9
algorithm9
Programming languages (2)
Github contributions (5)
tensorflow/privacy
Dec 2018 - Jan 2023
Library for training machine learning models with privacy for training data
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:12 releases, 6 reviews, 119 commits in 4 years 2 months
Contributions summary:Steve contributed to the `tensorflow/privacy` repository, focusing on implementing and modifying differentially private optimizers. Specifically, the user added and modified several files related to DP Adam and DP Gradient Descent optimizers, demonstrating a focus on integrating differential privacy into machine learning model training. The contributions involve modifications to the core optimizer files, including the use of a Gaussian Average Query and microbatching techniques. The changes aim to provide privacy guarantees for training data, reflecting a focus on privacy-preserving machine learning.
machine-learning-trainingprivacydifferential-privacymachine-learningtraining
schien1729/objax
Oct 2020 - Jan 2021
Contributions:2 PRs, 11 pushes, 1 branch in 3 months
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