Ziyi Mu
Software Engineer at Microsoft
Vancouver, British Columbia, Canada
Summary
🤩
Rockstar🎓
Top SchoolZiyi Mu is a software engineer with 11 years of experience building backend, infrastructure, and machine learning systems across AWS, Vimeo, and Microsoft. Currently contributing to M365 service backend and infra at Microsoft, Ziyi previously worked on machine learning infrastructure at AWS and payments at Vimeo, bringing production-grade reliability to diverse domains. He holds an MS from Columbia and a BSc from the University of Toronto, combining strong academic foundations with hands-on cloud experience. An active open-source contributor, Ziyi has improved core ML tooling in the widely used Apache MXNet project—adding GPU support for custom operators and strengthening build and test infrastructure. Pragmatic and detail-oriented, he focuses on bridging research-grade ML capabilities with scalable, maintainable backend services. Colleagues know him for quietly improving developer experience and turning complex infra issues into resilient systems.
11 years of coding experience
4 years of employment as a software developer
Master of Science (M.S.), Computer Science, Master of Science (M.S.), Computer Science at Columbia University in the City of New YorkBachelor of Science (BSc), Computer Science, Bachelor of Science (BSc), Computer Science at University of Toronto
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (10)
gpu-programming10
mxnet10
deep-learning10
python10
cprogramming-language9
c-language9
cuda8
cicd8
cmake7
fabric6
Programming languages (4)
Github contributions (5)
apache/mxnet
May 2019 - Dec 2020
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:4 reviews, 14 commits, 25 PRs in 1 year 7 months
Contributions summary:Ziyi primarily contributed to the development and enhancement of the MXNet deep learning framework. Their work involved fixing documentation, implementing GPU support for custom operators, and adding random number generator support within the custom operator libraries. They also addressed build issues, fixed tests and made improvements to the build infrastructure. Additionally, the user reverted a performance optimization related to memory access handling.
pythonschedulerdataflowmutationdata-science
rondogency/incubator-mxnet
Dec 2018 - Dec 2020
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Contributions:2 PRs, 103 pushes, 36 branches in 1 year 11 months
pythonschedulerdataflowmutationorchestration
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