Liqun Shao
Senior Machine Learning Engineer at Meta
Bellevue, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolLiqun Shao is a Senior Machine Learning Engineer and Ph.D.-trained computer scientist with a decade of experience building large-scale ML and NLP systems, currently leading LLM-based ads modeling at Meta. Previously he led science teams at Microsoft developing semantic LLMs for Copilot and spearheaded applied research in recommendation, explanation modeling, and meeting summarization. His background blends deep academic research in machine learning, data mining, and NLP with hands-on engineering in distributed systems (MapReduce, Flume) and production C++/Python development. He has a track record of shipping practical tooling and best-practice repos—contributing to Microsoft’s widely used nlp-recipes—while driving model training and Azure ML integrations. Known as a pragmatic tech lead and communicator, he pairs algorithmic rigor with operational experience in real-time anomaly detection and large-scale semantic modeling. Based in Bellevue, WA, he brings research depth and production instincts to teams turning cutting-edge NLP into robust, scalable products.
10 years of coding experience
9 years of employment as a software developer
UMass Lowell
English, Chinese
Github Skills (10)
pytorch10
machine-learning10
nlp10
natural-language-processing10
python9
deeplearning-ai9
deep-learning9
guideline7
plaintext7
mlops6
Programming languages (3)
Github contributions (5)
microsoft/nlp-recipes
Jun 2019 - Aug 2019
Natural Language Processing Best Practices & Examples
Role in this project:
ML Engineer
ML EngineerContributions:100 commits in 2 months
Contributions summary:Liqun made several changes to the `gensen_aml_deep_dive.ipynb` notebook, including modifying file paths, adding Azure Machine Learning (AML) utility functions, and correcting training results explanations. These changes were primarily within a notebook environment, focused on a sentence similarity project using the GenSen model. The commits also included adjustments to the `train.py` file, suggesting involvement in the model training process and performance analysis within the Azure ML environment.
natural-language-understandingnluword-embeddingslanguage-processingdialogue-systems
catherine667/Automatic-Text-Summarization-and-Title-Generation
Feb 2017 - Feb 2017
Contributions:8 commits, 7 pushes, 1 branch in 1 day
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