Klaus Ma is a Principal Software Engineer and systems architect with over a decade of experience building cloud-native, batch and high-performance systems across firms like NVIDIA, Huawei, IBM and Baidu. He founded Volcano and kube-batch and served as an emeritus leader of Kubernetes SIG-Scheduling and CNCF working groups, evidencing deep influence on container scheduling and runtime standards. Klaus blends hands-on backend development—contributing to core Kubernetes APIs and cloud-native batch schedulers—with product and team leadership, having led global teams and shaped batch container architectures. His work spans cloud computing, machine learning, big data and financial services, frequently focusing on scheduler extensibility, resource modeling and build/integration tooling. Based in Beijing, he combines academic roots from Jilin University with practical open-source stewardship that has materially advanced Kubernetes scheduling for AI/ML and HPC workloads.
10 years of coding experience
16 years of employment as a software developer
Master Computer Science and technology, Master Computer Science and technology at Jilin University
A batch scheduler of kubernetes for high performance workload, e.g. AI/ML, BigData, HPC
Role in this project:
Back-end Developer
Contributions:9 releases, 2 reviews, 601 commits in 5 years
Contributions summary:Klaus's commits primarily involve integrating and merging changes to various configuration and build scripts. These scripts, written in Bash and Golang, seem to be related to build process, ensuring correct functionality, and deepcopy generation within the kubernetes-incubator/kube-arbitrator project, which is a batch scheduler for Kubernetes. These changes indicate the user's involvement in the project's backend, integration, and build-related infrastructure.
Contributions:3 releases, 186 reviews, 189 commits in 1 year 11 months
Contributions summary:Klaus primarily contributed to the back-end functionality of the "volcano" project, a Cloud Native Batch System. Their commits added new capabilities like queue management and implemented code improvements. The user also made changes to the scheduling API and included resource-related functionalities, such as adding resources to the PodGroup's MinResources.
golangbigdatabatchmachine-learningkubernetes
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