Mark Yang
DevOps Engineer - Applied Machine Learning at TikTok
Singapore
Summary
👤
Senior🎓
Top SchoolMark Yang is a DevOps and applied machine learning engineer with 9 years of experience building resilient CI/CD, observability, and large-scale inference platforms, currently focused on distributed parameter servers and inference services at TikTok. He has led SRE and DevOps teams at iHerb, driving GitOps migrations with ArgoCD, Terraform/Terragrunt, and EKS while architecting developer portals and CD pipelines that deployed thousands of Redis instances. Comfortable as both an individual contributor and manager, he combines deep systems debugging from his Microsoft background with hands-on software skills in C#/.NET, Go, Python, and TypeScript. His work repeatedly emphasizes practical observability—APM, logging, and tracing—to reduce time-to-resolution and elevate developer experience. Based in Singapore, he pairs a detail-oriented engineering practice with a track record of turning large, complex platform initiatives into incremental, testable deliverables. An entrepreneurially minded technologist, he also founded an education startup teaching kids programming and LEGO-based engineering.
9 years of coding experience
15 years of employment as a software developer
BS, Computer Science and Technology, BS, Computer Science and Technology at Nanjing Normal University
English, Chinese
Github Skills (47)
attention-mechanism10
deeplab10
encoder9
integer9
computer-vision8
classification8
deep-learning8
reinforcement-learning8
segmentation8
image-classification7
pytorch7
artificial-intelligence7
lov7
tensorflow7
loss-functions7
Programming languages (4)
Github contributions (5)
MarkYangjiayi/Semantic-Quantization
Jan 2019 - Jan 2020
Tensorflow implementation for Training and Inference for Integer-based Semantic Segmentation
Contributions:31 commits, 28 pushes, 1 branch in 1 year
semantic-segmentationdeep-learningintegerinferencetraining
MarkYangjiayi/MarkYangjiayi.github.io
Dec 2018 - Sep 2019
Contributions:99 pushes in 9 months
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