Wah Keng
AI Engineer III at Electronic Arts (EA)
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolWah Keng is an AI Engineer III in New York with 11 years of experience building production-grade ML systems, specializing in Kubernetes, MLOps, system design, PyTorch, deep learning and reinforcement learning. He has driven revenue-impacting projects across gaming and ad-tech at companies like EA, AppLovin and MZ, combining model development with cloud-native deployment (Helm, k8s) and ML DevOps. An active open-source contributor, he improved core NLP parsing in the widely used spaCy library and added practical examples, CI and tests to TensorFlow-focused projects, reflecting a strong focus on robustness and reproducibility. Trained in mathematics and computer science, he brings research fluency—from computational geometry to quantum foundations—into pragmatic engineering solutions. Outside work he balances engineering rigor with rock climbing, hinting at a methodical risk-taker who favors elegant, tested solutions.
11 years of coding experience
9 years of employment as a software developer
Top 5% of graduating class, Top 5% of graduating class at Chung Ling High SchoolBachelor's degree, Mathematics and Computer Science, Bachelor's degree, Mathematics and Computer Science at Lafayette College
English, Chinese, Chinese, hokkien, Malay
Github Skills (21)
javascript10
parser10
lib10
pytest10
python10
data-science10
testing10
date-parsing10
machine-learning10
deep-learning10
tensorflow10
parsing10
parse10
natural-language-processing10
cython10
Programming languages (16)
Github contributions (5)
matthewmueller/date
Jan 2016 - Mar 2016
Date() for humans
Role in this project:
Back-end Developer
Back-end DeveloperContributions:53 commits, 4 PRs, 11 comments in 1 month
Contributions summary:Wah primarily contributed to the development and maintenance of the `date` library. Their work included fixing issues related to date and time parsing, specifically addressing minute-related problems. They implemented natural language normalization using a dedicated module and integrated external libraries like Lodash to enhance the library's functionality. Furthermore, the user added tests to improve code quality.
datetimefor-humanscalendar
tflearn/tflearn
Sep 2016 - Sep 2016
Deep learning library featuring a higher-level API for TensorFlow.
Role in this project:ML Engineer
ML EngineerContributions:12 commits, 4 PRs, 3 comments in 15 days
Contributions summary:Wah primarily contributed to the examples and utilities within the tflearn library, focusing on improving its usability and testing. The user corrected a regression example by ensuring the correct input format for the `predict` function, and also added a finetuning example using a VGG network. Furthermore, the user added continuous integration capabilities through Travis CI and enhanced the testing setup with pytest coverage reports.
apidata-sciencedeep-learningmachine-learningdeep-learning-library
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