Michael Hu
Senior Software Engineer at Google
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Hu is a Senior Software Engineer with 12 years of hands-on experience building and deploying production ML systems at Google, specializing in forecasting, foundation-model tuning, and generative AI evaluation. He combines strong MLOps expertise—Dataflow, BigQuery, Composer/KFP, CAIP/AutoML—with practical engineering across backend and cloud serving, and has driven Vertex AI forecasting features and pipeline improvements in prominent open-source projects like googleapis/python-aiplatform, TFX, and Kubeflow Pipelines. His work spans the full ML lifecycle from time-series AutoML and holiday-aware forecasting to scikit-learn integration and evaluation components, reflecting a knack for turning research-grade models into reliable, test-covered production components. Based in New York, he pairs an MEng from Cornell with early startup and research experience, and brings an uncommon combination of production-focused rigor and open-source collaboration to large-scale ML engineering.
12 years of coding experience
7 years of employment as a software developer
Master of Engineering - MEng Computer Science, Master of Engineering - MEng Computer Science at Cornell UniversityBachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Northeastern University
English, Chinese, French
Stackoverflow
126reputation
6kreached
6answers
0questions
Github Skills (27)
kubernetes10
google-cloud-platform10
python10
scikit10
tfx10
kubeflow10
machine-learning10
gcp10
mlops10
time-series-forecasting10
tensorflow10
automl10
kubernetes-pods10
scikit-learn10
vertex-ai10
Programming languages (4)
Github contributions (5)
kubeflow/pipelines
Feb 2020 - Dec 2022
Machine Learning Pipelines for Kubeflow
Role in this project:
MLOps Engineer
MLOps EngineerContributions:20 commits, 1 PR, 7 comments in 2 years 10 months
Contributions summary:Michael primarily contributed to the Kubeflow Pipelines project by removing and refactoring forecasting components related to AutoMLForecastingTrainingJob, streamlining functionality. Their work involved converting forecasting component types, fixing unit tests, and updating dependencies. The contributions indicate a focus on improving and maintaining the pipelines' machine learning capabilities, specifically within the forecasting domain.
pipelinetektondata-sciencemachine-learningmlops
googleapis/python-aiplatform
Mar 2022 - Oct 2022
A Python SDK for Vertex AI, a fully managed, end-to-end platform for data science and machine learning.
Role in this project:ML Engineer
ML EngineerContributions:20 reviews, 8 commits, 8 PRs in 6 months
Contributions summary:Michael primarily contributed to the documentation and implementation of features related to the Vertex AI Forecasting service within the Google Cloud Platform. Their work involved updating documentation to include time dependency details, and allowing users to specify timestamp splits. They added new features like holiday region support, and hierarchy and window configurations, demonstrating expertise in AutoML and time series model training.
pythonend-to-endsciencedata-sciencemachine-learning
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