Hao Yuan
Principal Software Engineer at Microsoft
China
Summary
🤩
Rockstar🎓
Top SchoolHao Yuan is a Principal Software Engineer at Microsoft with 11 years of experience building cloud-native infrastructure and orchestration for large-scale AI workloads. He has progressed through senior engineering and leadership roles at Microsoft, Huawei, and Oracle, focusing on Kubernetes, resource scheduling, and production automation. At Microsoft he contributes to Azure Kubernetes Service and has hands-on open-source experience on microsoft/pai, where he improved cluster resource management, Docker automation, and job environment/auth handling for AI workloads. Known for bridging DevOps and ML engineering, he combines low-level automation (Dockerfiles, scripts) with cluster-level design (memory limits, services) to make ML platforms reliable and scalable. Based in China and grounded in a technical bachelor’s from the University of Science and Technology Beijing, he brings practical expertise in turning complex distributed systems requirements into maintainable production solutions.
11 years of coding experience
10 years of employment as a software developer
Bachelor, Bachelor at University of Science and Technology Beijing
Github Skills (18)
kubernetes10
resource-management10
automation10
kubernetes-pods10
cluster-manager10
cluster-api10
automations10
clustering10
scripting9
dockers9
docker9
scheduling9
shell9
script9
sh9
Programming languages (7)
Github contributions (5)
microsoft/pai
Jun 2018 - Apr 2019
Resource scheduling and cluster management for AI
Role in this project:
DevOps Engineer & ML Engineer
DevOps Engineer & ML EngineerContributions:2 releases, 224 commits, 184 PRs in 10 months
Contributions summary:Hao contributed to the project by implementing and modifying various aspects of the deployment and configuration of the PAI platform. This includes adding memory limits and resource requests, and modifying Kubernetes services for the cluster. The user also added and modified Dockerfiles and shell scripts for automation and monitoring. Additionally, the user added job environment configurations and improved the handling of authentication tokens, indicating contributions to both infrastructure and job management.
cluster-managementmodel-trainingchainertensorflowcloud
hao1939/DLWorkspace
Apr 2019 - Sep 2019
Deep Learning Workspace
Contributions:19 PRs, 297 pushes, 30 branches in 5 months
caffe2deep-learningworkspaceneural-networksmachine-learning
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