Systems Software Engineer, Kubernetes Scale - DGX Cloud — NVIDIA (ufficio Zurich)

Role overview

The DGX Cloud organization at NVIDIA brings together cutting-edge hardware and software innovation to deliver industry-leading accelerated computing for the world's most adventurous AI workloads. We're a team of innovative engineers dedicated to solving some of the world's biggest challenges, constantly driving advancements, and impacting millions of lives worldwide!

We are looking for an outstanding Systems Software Engineer with deep experience in distributed systems, open-source technologies such as Kubernetes and containers, and a strong background in systems performance and scalability. The ideal candidate brings broad, end-to-end experience across the stack - from GPU operator and device plugins to distributed inference serving and cloud platforms - along with the technical depth to investigate and address exciting, real-world problems at scale. In this pivotal role, you will take on the challenge of scaling AI infrastructure while optimizing total cost of ownership, driving down cost per token to unlock the next generation of AI innovation and AI factories!

• Drive end-to-end performance and scale characterization for the NVIDIA DGX Cloud software stack, from Kubernetes control and data planes through NVIDIA components such as GPU Operator, Network Operator, DCGM, NIM, and distributed inference serving, following issues from orchestration down to the metal.

• Collaborate with AI researchers, developers and customers to develop innovative, automated tests that simulate real user workloads using custom-built and leading open-source tools and frameworks.

• Deep dive into performance and scale issues in complex distributed systems, including interactions between Kubernetes and the NVIDIA software stack, to identify and resolve root causes.

• Design and develop monitoring, reporting and analysis tools for performance and scale testing across software, GPU and CPU resources.

• Triage, debug and root cause issues related to operating Kubernetes clusters at ultra-large scale, ensuring reliability and efficiency.

• Build and maintain a high-velocity framework that enables continuous, always-on performance and scale testing via a modern CI/CD pipeline.

• Document research, methodologies and results clearly and concisely, and present findings at internal and external venues, including community conferences such as KubeCon and GTC.

• Engage efficiently with upstream communities — including Kubernetes, CNCF and NVIDIA open-source projects — to validate performance and scalability of AI workloads early and help shape design and development decisions.