Hai Ning
Principal AI ML Customer Engineer at Google
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolHai Ning is a Principal AI/ML customer engineer and seasoned technology leader with 11 years delivering large-scale cloud and machine learning platforms across Google, Meta, Amazon, and Microsoft. He has led cross-functional teams to productionize petabyte-scale data lakes and GPU-backed ML workflows that power consumer products at global scale, and shaped flagship offerings like Azure Machine Learning Studio from concept to GA. Equally comfortable in product, program, and hands-on engineering roles, he contributes to Azure ML example notebooks and has deep experience with Python, Docker, Spark, and TensorFlow. Based in Cambridge, MA, he combines rigorous academic training from Tsinghua and MIT with a knack for turning complex research-grade ML into reliable, privacy-conscious production systems. A pragmatic builder and recruiter, he’s actively hiring engineering and program leaders for advanced ML data-platform work.
11 years of coding experience
16 years of employment as a software developer
Ph.D. Information Technology, Ph.D. Information Technology at Massachusetts Institute of TechnologyBachelor of Computer Science, Bachelor of Computer Science at Tsinghua University
English, Chinese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
machine-learning10
azure10
microsoft-azure10
python10
azure-machine-learning10
deep-learning9
notebook9
pyspark8
data-science8
dockers7
docker7
conda7
devops6
Programming languages (7)
Github contributions (5)
Azure/MachineLearningNotebooks
Sep 2018 - Feb 2019
Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
Role in this project:
ML Engineer & DevOps Engineer
ML Engineer & DevOps EngineerContributions:69 commits, 8 PRs, 68 pushes in 5 months
Contributions summary:Hai primarily contributed to the development and configuration of machine learning examples within Azure Machine Learning notebooks. Their work involved updating notebook configurations, including setting up compute targets like Azure Container Instances (ACI) and HDInsight, specifying dependencies, and submitting Spark jobs. They also made changes to the training scripts and data handling components of the notebooks.
pythontensorflowmicrosoftsdk-pythondata-science
hning86/articles
Aug 2016 - May 2017
Contributions:62 commits, 71 pushes, 13 branches in 8 months
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