Ning Gao
Research Scientist at Meta
Kirkland, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolNing Gao is a Research Scientist with 11 years of experience designing and operating scalable, fault-tolerant distributed systems, currently applying expertise in distributed consensus, transactions, and reconfiguration at Meta. He has a strong background in cloud ML infrastructure from his time on Google Cloud AI Platform and hands-on contributions to the popular Kubeflow Pipelines project, where he improved TF Serving deployments and CI/test reliability. Ning’s research roots—culminating in a PhD from University of Colorado Boulder—inform practical system designs like transactional spatial indexes and database services built earlier in his career. Comfortable across backend, storage, and DevOps tooling, he combines production-grade engineering with research rigor to solve complex availability and consistency challenges. Based in Kirkland, WA, he brings a rare blend of academic depth and proven impact on open-source ML infrastructure and distributed database systems.
11 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Colorado BoulderBachelor's degree, Computer, Bachelor's degree, Computer at Zhejiang University
Chinese, English
Github Skills (12)
sh10
kubernetes10
script10
shell10
python10
scripting10
kubernetes-pods10
gcp10
devops9
debug9
debugging9
testing8
Programming languages (9)
Github contributions (5)
kubeflow/pipelines
Nov 2018 - Jan 2020
Machine Learning Pipelines for Kubeflow
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:6 releases, 184 commits, 227 PRs in 1 year 2 months
Contributions summary:Ning primarily focused on bug fixes and debugging within the `kubeflow/pipelines` repository. They addressed issues related to the deployment of TF Serving, indicating involvement in machine learning model serving infrastructure. Additionally, the user contributed to the test suite with changes to scripts and sample tests. They also made code changes to the test/presubmit-tests.sh scripts indicating working knowledge of the project's build process and DevOps tools.
pipelinetektondata-sciencemachine-learningmlops
gaoning777/pipelines
Dec 2018 - Jan 2020
Machine Learning Pipelines for Kubeflow
Contributions:3 PRs, 520 pushes, 270 branches in 1 year 1 month
data-sciencemachine-learningmlopskedrokubeflow
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