Jiyong Jung
Seoul, South Korea
Summary
🤩
Rockstar🎓
Top SchoolJiyong Jung is a seasoned software engineer and technical leader with 12 years of experience building production-grade backend systems, ML infrastructure, and AI-driven product features for companies like Google and Woowa Bros. He excels at delivering fast, reliable outcomes across the stack—from UNIX-based core services and search engines to web services and ML pipelines—and has led teams responsible for search, accounts, checkout, recommendations and computer-vision enhancements in a major Korean consumer app. At Google he contributed to TensorFlow Extended and downstream tooling, and his open-source work includes localization of TensorFlow docs and practical fixes in Kubeflow and TFX that improve RFC-compliance, dependency stability, and CI robustness. Known for blending hands-on engineering with infrastructure and test improvements, he brings particular strength in information retrieval and knowledge handling across heterogeneous data types. Based in Seoul, he combines deep systems experience (C/C++, Go, Ruby, Java) with a pragmatic focus on shipping production ML solutions.
12 years of coding experience
17 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at Korea Advanced Institute of Science and Technology
Korean, English
Stackoverflow
1reputation
0reached
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Github Skills (30)
dependency-management10
kubernetes10
data-pipelines10
translation10
python10
kubeflow10
machine-learning10
internationalization10
lang10
localization10
tensorflow10
pipe10
pipeline10
kubernetes-pods10
anylanguage10
Programming languages (6)
Github contributions (5)
tensorflow/tfx
Jan 2020 - Jan 2023
TFX is an end-to-end platform for deploying production ML pipelines
Role in this project:
ML Engineer & DevOps Engineer
ML Engineer & DevOps EngineerContributions:113 reviews, 508 commits, 26 PRs in 3 years 1 month
Contributions summary:Jiyong contributed to the TFX framework by improving the robustness of tests, fixing build errors, and optimizing status polling frequency. They addressed issues in the container building process, specifically with reused images, and enhanced the CI/CD pipeline with custom testing loops to reduce polling frequency. Their work demonstrates a focus on testing and infrastructure improvements.
deployingend-to-endml-pipelinesmlmlops
tensorflow/model-analysis
Nov 2019 - Oct 2022
Model analysis tools for TensorFlow
Role in this project:ML Engineer
ML EngineerContributions:9 commits in 2 years 11 months
Contributions summary:Jiyong's contributions primarily involve maintaining and upgrading dependencies related to TensorFlow and its ecosystem, including tfx-bsl, pyarrow, and NumPy. They addressed compatibility issues with different TensorFlow versions and TensorFlow Text, ensuring the project's continued functionality. The user also updated the project's TensorFlow version dependency to the latest available. These commits demonstrate a focus on project maintenance and adapting to changes in the TensorFlow environment.
analysis-toolsmachine-learningmodel-analysistensorboardtensorflow
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