Yiyong Lin
Senior Software Engineer at Microsoft
Bellevue, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolYiyong Lin is a Senior Software Engineer based in Bellevue, Washington with a decade of experience building large-scale distributed systems at Microsoft. He progressed through research and product engineering roles—including Microsoft Research Asia and Bing Ads—bringing a research-informed approach to production services. His contributions to the well-known microsoft/multiverso parameter-server project show deep expertise in distributed ML infrastructure, YARN integration, and container orchestration for coordinated worker/server setups. Yiyong excels at bridging low-level systems engineering with higher-level service reliability, often improving deployment and file-management workflows that enable scalable ML workloads. He holds degrees from Beijing University of Posts and Telecommunications and Peking University, combining strong academic foundations with practical, production-grade implementations. Colleagues describe him as a pragmatic problem-solver who turns complex distributed challenges into maintainable, automated solutions.
10 years of coding experience
6 years of employment as a software developer
Bachelor's degree, Network Engineering, Bachelor's degree, Network Engineering at Beijing University of Posts and TelecommunicationsMaster's Degree, Master's Degree at Peking University
Github Skills (11)
yarn-berry10
yarn210
javas10
distributed-systems10
java10
yarn310
yarnpkg10
hadoop9
bash8
script8
machine-learning8
Programming languages (4)
Github contributions (5)
microsoft/Multiverso
Nov 2015 - Jun 2016
Parameter server framework for distributed machine learning
Role in this project:
Back-end Developer
Back-end DeveloperContributions:24 commits, 3 pushes in 6 months
Contributions summary:Yiyong primarily focused on implementing and modifying core functionalities related to the Distributed Machine Learning (DMTK) framework within the `microsoft/multiverso` repository. Their contributions involve generating machine lists and server configurations for distributed environments, alongside modifications to the container launching and management processes. They demonstrate a strong understanding of the YARN framework and its integration within the distributed machine learning system, specifically addressing the setup and coordination of workers and servers. This includes the addition and modification of command files to allow for proper file management and execution of the core distributed application.
parameter-servermachine-learningparameterserver-frameworkdistributed-machine-learning
yiylin/Recognizers-Text
Jun 2017 - Mar 2018
Microsoft.Recognizers.Text.Number provides robust recognition and resolution of numbers expressed in English, Spanish and Chinese.
Contributions:1 release, 12 PRs, 44 pushes in 9 months
chineserecognitionrecognizersresolutionenglish
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