Joe L
Software Engineer - DV360 at Google
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolJoe L is a software engineer at Google with six years of experience building cloud-native AI systems and front-end tooling. He has driven scalable UIs for Kubeflow Pipelines and air-gapped Distributed Cloud platforms, and recently focused on LLM resource management for Gemini traffic prioritization before moving to DV360 reporting. Comfortable across ML infrastructure and frontend engineering, he contributed notable UX and data-format work to the widely used kubeflow/pipelines project, including KFPv2 UI enablement and JSON→YAML pipeline spec conversion. A UW alumnus with a background in materials science and battery-tech curiosity, he blends research-rooted rigor with production engineering. Colleagues describe him as pragmatic and detail-oriented, often surfacing small UX and performance wins that improve platform operability.
6 years of coding experience
2 years of employment as a software developer
Master of Science - MS Chemical Engineering: Data Science, Master of Science - MS Chemical Engineering: Data Science at University of WashingtonSummer Research Solar Cell Research & Studies, Summer Research Solar Cell Research & Studies at Lanzhou UniversityBachelor of Science - BS Materials Science and Engineering, Bachelor of Science - BS Materials Science and Engineering at National Tsing Hua University
Chinese, German, English
Github Skills (11)
uid10
typescript10
javascript10
typescripts10
front-end-development10
ui-design10
react10
typescript-types10
yaml9
kubeflow-pipelines9
machine-learning6
Programming languages (4)
Github contributions (5)
kubeflow/pipelines
Apr 2022 - Jan 2023
Machine Learning Pipelines for Kubeflow
Role in this project:
Front-end Developer
Front-end DeveloperContributions:1 release, 147 reviews, 21 commits in 9 months
Contributions summary:Joe's commits primarily focus on front-end development within the Kubeflow Pipelines project. They implemented new features related to feature flags, converting data formats from JSON to YAML for pipeline specifications, and enabling the KFPv2 UI. Their work included modifying existing components, such as RunDetailsV2 and StaticNodeDetailsV2, and adding or modifying components to improve the UI and user experience. The user demonstrated expertise in updating components, and modifying code to align with backend changes.
pipelinetektondata-sciencemachine-learningmlops
jlyaoyuli/pipelines
Apr 2022 - Nov 2023
Machine Learning Pipelines for Kubeflow
Contributions:2 PRs, 285 pushes, 118 branches in 1 year 8 months
data-sciencemachine-learningmlopskedrokubeflow
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.