Ke Huang
Machine Learning Engineer at Facebook
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolKe Huang is a Machine Learning Engineer with nearly a decade of industry experience, currently building NLP and ML solutions at Facebook and formerly leading enterprise-scale AI projects in Azure Global at Microsoft. He specializes in productionizing end-to-end NLP systems—voice assistants, QA chatbots, large-scale document analysis, and resume-job matching—demonstrating measurable business impact like cutting candidate matching from two weeks to 76 seconds and boosting conversion rates dramatically. With a PhD in Computer Science and over ten research publications, he bridges rigorous research (mobile sensing, context awareness) with scalable cloud-native engineering on Azure. An active contributor to Microsoft’s widely used nlp-recipes, he’s implemented token classification and added datasets to improve NER workflows for transformer models. Curious and inventive, he continually explores side projects that extend ML capabilities beyond standard pipelines. Based in the Greater Seattle Area, he combines academic depth with practical results in high-throughput, low-latency production systems.
9 years of coding experience
10 years of employment as a software developer
Bachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at East China Jiaotong UniversityDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of Massachusetts at Lowell
Chinese, English
Github Skills (14)
transformers10
named-entity-extraction10
tokenize10
pytorch10
machine-learning10
tokenizer10
nlp10
named-entity-recognition10
entity-extraction10
python10
natural-language-processing9
pre-trained-model9
deep-learning9
guideline8
Programming languages (3)
Github contributions (5)
microsoft/nlp-recipes
Aug 2019 - Dec 2019
Natural Language Processing Best Practices & Examples
Role in this project:
ML Engineer
ML EngineerContributions:49 commits, 11 PRs, 29 pushes in 4 months
Contributions summary:Ke focused on implementing and refining token classification for named entity recognition (NER) tasks within the nlp-recipes repository. They added code for token classification, specifically related to named entity recognition, and updated existing code to align with comments and improve functionality. The user also added a new dataset (wikigold) to load directly into the transformer models and evaluate the NER models.
natural-language-understandingnluword-embeddingslanguage-processingdialogue-systems
Azure-Samples/MachineLearningSamples-DocumentCollectionAnalysis
Sep 2017 - Feb 2018
MachineLearningSamples-DocumentCollectionAnalysis
Contributions:9 commits, 6 PRs, 8 pushes in 5 months
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