Jing Zhang
Senior Software Engineer at Google
Waterloo, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolJing Zhang is a Senior Software Engineer based in Waterloo, Ontario with seven years of professional experience and a Ph.D. in Computer Science from Tsinghua University. Currently at Google, she focuses on back-end systems and MLOps, bringing research-grade rigor to production engineering. Her open-source contributions to the high-profile Kubeflow Pipelines project include building TensorBoard CRD helpers, Kubernetes service account setups, and visualization deployments that improve pipeline observability. Jing combines deep academic training with practical cloud-native engineering, often bridging ML tooling and platform reliability. Colleagues value her for shipping core infrastructure features that make complex ML workflows easier to manage and visualize. An understated strength is her knack for turning niche research concepts into robust, reusable platform components.
7 years of coding experience
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Tsinghua University
English, Chinese
Github Skills (14)
kubernetes10
mlops10
kubernetes-pods10
apidoc9
tensorflow9
api9
go8
rest-api8
machine-learning8
api-rest8
restful-api8
api-design8
cicd7
python6
Programming languages (7)
Github contributions (5)
kubeflow/pipelines
Jun 2019 - Sep 2020
Machine Learning Pipelines for Kubeflow
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:8 releases, 7 reviews, 92 commits in 1 year 3 months
Contributions summary:Jing primarily contributed to the back-end of the Kubeflow Pipelines project. They implemented a helper function to create TensorBoard CRDs and integrated it with existing components. They also worked on setting up Kubernetes service accounts and deploying a visualization server. The user's work included setting user credentials and handling filtering of experiment runs. Their contributions show a focus on core functionality related to pipeline visualization and management.
pipelinetektondata-sciencemachine-learningmlops
jingzhang36/pipelines
Apr 2019 - Jun 2019
Machine Learning Pipelines for Kubeflow
Contributions:48 pushes, 15 branches in 2 months
data-sciencemachine-learningmlopskedrokubeflow
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