Mingbo Wan
Production Engineer at Meta
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMingbo Wan is a veteran production and SRE leader with over two decades of experience building and operating large-scale internet and AI infrastructure from Silicon Valley to global enterprises. He has led and scaled SRE and infrastructure teams at Meta (Reality Labs), TikTok, and LeetCode, driving reliability, cost management, and developer velocity across multi-cloud Kubernetes environments. Equally comfortable in hands-on automation, he has contributed DevOps work to high-profile open-source projects such as the PyTorch build system, improving build and smoke-test automation for CUDA-enabled binaries. Mingbo’s background spans mission-critical uptime at semiconductor manufacturing, game-scale production at Electronic Arts, and introducing container and config-management practices at Samsung, giving him deep operational and tooling instincts. Known for building high-performing teams and pragmatic automation, he combines strategic direction with a willingness to dive into build scripts and performance tuning when it matters. Based in San Jose, he pairs an academic foundation from Fudan with a proven track record of delivering reliable systems at internet scale.
13 years of coding experience
22 years of employment as a software developer
Master Computer Application, Master Computer Application at Fudan UniversityBachelor Mechanical engineering, Bachelor Mechanical engineering at Jiangsu Polytechnic University
Github Skills (13)
cuda10
bash-shell10
sh10
pytorch10
bash10
script10
shell10
build-automation10
cicd10
docker8
dockers8
python8
aws-s37
Programming languages (6)
Github contributions (5)
pytorch/builder
Sep 2019 - Oct 2019
Continuous builder and binary build scripts for pytorch
Role in this project:
DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:12 commits, 23 PRs, 5 pushes in 28 days
Contributions summary:Mingbo primarily contributed to automating the build and testing process for the PyTorch project. They implemented changes to build scripts, adjusted configuration files, and updated smoke tests to support CUDA versions and package types. The user's work involved adapting build configurations, ensuring correct package names, and addressing paths within the testing environment. They also focused on fixing issues related to the libtorch smoke test.
pytorchbuild-scriptscontinuousbuilder
mingbowan/pytorch
Jul 2019 - Jan 2020
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:269 pushes, 69 branches in 5 months
pythongpu-accelerationdeep-learninggpuacceleration
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