Our teams mission is to make Kubernetes the best platform for AI workloads. We work upstream with the Kubernetes and CNCF communities to design new APIs, build production-grade implementations, and shape the future of cloud-native infrastructure for AI.
As a Senior Software Engineer, you'll tackle the most challenging problems at the intersection of Kubernetes, distributed systems, and AI infrastructure.
What you'll be doing:
Design, implement, and upstream new capabilities for Kubernetes and CNCF projects.
Collaborate with engineering teams across us to identify AI infrastructure challenges and solve them through upstream innovation.
Work closely with Kubernetes SIGs, Working Groups, and the broader open-source community to design and implement new capabilities.
Participate in architecture discussions, API design, technical proposals, and code reviews.
Build reliable, scalable infrastructure software for next-generation AI workloads.
Write clear technical documentation and design proposals.
What we need to see:
B.Sc/M.Sc or higher in Computer Science, Computer Engineering, or a related field or equivalent practical experience.
5+ years of software engineering experience building distributed systems, cloud infrastructure, or platform software.
Deep understanding of Kubernetes and the cloud-native ecosystem.
Experience extending, building, or contributing to Kubernetes-based solutions.
Strong communication skills and the ability to collaborate across engineering teams and open-source communities.
Curiosity, adaptability, and an interest in learning AI infrastructure.
Ways to stand out from the crowd:
Active contributor to Kubernetes, CNCF, or other open-source infrastructure projects.
Leadership experience in open-source communities, such as maintainer, reviewer, approver, SIG/WG leadership, or similar roles.
Experience designing Kubernetes APIs, authoring KEPs, or driving community proposals through upstream processes.
Experience with Kubernetes scheduling, networking, Gateway API, workload APIs, or other core Kubernetes subsystems.
Understanding of AI infrastructure, including inference systems, GPU scheduling, distributed serving, or LLM infrastructure.
