Jenny Lee
Senior Software Engineer at Google
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJenny Lee is a Senior Software Engineer in Sunnyvale with a decade of experience building production-grade ML and software systems, currently at Google. She brings deep research roots from MIT—where she developed state-of-the-art LSTM/CNN models for NLP problems and won the Dialog State Tracking Challenge—combined with hands-on engineering that spans model design, tokenization, and robust training practices. Her open-source contributions to a prominent NER project show attention to reproducibility and model reliability (gradient clipping, BIOES tagging, refactoring to preserve original data). Jenny’s background in control theory, embedded systems, and field engineering gives her uncommon breadth across algorithms, systems, and hardware-aware debugging. She excels at turning research ideas into maintainable, production-ready code and often focuses on data integrity and architecture tweaks that materially improve model behavior.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Massachusetts Institute of TechnologyCalifornia Institute of Technology
English, Korean, Chinese, Japanese, French
Github Skills (6)
neural-network10
machine-learning10
nlp10
deep-learning10
tensorflow10
python10
Programming languages (1)
Github contributions (2)
Franck-Dernoncourt/NeuroNER
Apr 2017 - Jul 2017
Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
Role in this project:
ML Engineer
ML EngineerContributions:24 commits, 11 pushes, 2 comments in 2 months
Contributions summary:Jenny primarily contributed to the development and improvement of a named-entity recognition system. Their work involved adding features like gradient clipping and the BIOES tagging format, indicating efforts to refine model training and output representation. Furthermore, they refactored existing code to avoid modifying original data, which suggests a focus on code maintainability and data integrity. The user also integrated a Stanford tokenizer and made modifications to the model's architecture, showing a clear involvement in model development and optimization.
nlppytorchnamed-entity-recognitionartentity-recognition
Franck-Dernoncourt/git_sandbox
Jul 2015 - Jul 2015
Contributions:6 pushes in 1 day
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