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Senior Software Engineer

NVIDIA·Israel·en
Not specifiedFull-timeFrontend DevelopmentComputer Hardware Manufacturing

Seeking a Senior Software Engineer to design, implement, and upstream new capabilities for Kubernetes and CNCF projects, enabling next-generation AI infrastructure. Collaborate with engineering teams and open-source communities to solve complex distributed systems challenges.

Responsibilities

  • Design, implement, and upstream new capabilities for Kubernetes and CNCF projects.
  • Collaborate with engineering teams 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.

Requirements

  • 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 ability to collaborate across engineering teams and open-source communities.
  • Curiosity, adaptability, and interest in learning AI infrastructure.

Nice to have

  • 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.

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