Mo Kweon
Software Engineer at Google
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMo Kweon is a Software Engineer in San Jose with 10 years of experience building machine learning systems and production web applications, currently at Google. He blends applied ML expertise—contributing to well-known TensorFlow tutorial repos with fixes to backprop, softmax, and RNN layers—with front-end and full-stack experience migrating large UIs from AngularJS to React+TypeScript. His background spans data analysis and automation for gaming KPIs, fraud detection using ML, and operational ETL pipelines, reflecting a strong product-oriented mindset. Educated in economics and applied math at UC Berkeley and pursuing an MS in Computer Science at Georgia Tech, he brings quantitative rigor to engineering problems. Colleagues rely on him to translate research-grade models into maintainable code and dashboards, and his public projects showcase pragmatic, well-documented improvements rather than one-off experiments.
10 years of coding experience
2 years of employment as a software developer
BA, Economics, Applied Mathematics, BA, Economics, Applied Mathematics at University of California, BerkeleyMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at Georgia Institute of Technology
English, Korean
Stackoverflow
1,852reputation
668kreached
16answers
6questions
kubernetes
top-5%
keras
top-5%
Github Skills (17)
python10
machine-learning10
tensorflow10
kubernetes9
n9
rnn-model9
backpropagation9
keras9
gitlab6
react-router6
q-learning6
react6
deep-learning6
theano6
numpy6
Programming languages (30)
Github contributions (5)
hunkim/DeepLearningZeroToAll
Mar 2017 - May 2020
TensorFlow Basic Tutorial Labs
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 38 commits, 87 PRs in 3 years 2 months
Contributions summary:Mo primarily contributed to the implementation and improvement of machine learning models within the TensorFlow framework. Their work included fixing label and backpropagation issues, refactoring the softmax implementation, and updating variable initialization methods. Further contributions involved refactoring and documenting of sigmoid and MinMaxScaler implementations, alongside the addition of a fully connected layer to the RNN model for stock price prediction.
pytorchmxnetpythonmachine-learningtensorboard
deepbaksu/conversion
Jan 2020 - Oct 2021
Conversion library for Go
Contributions:2 reviews, 29 commits, 11 PRs in 1 year 9 months
golangconversiongolang-cligo
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