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NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years, driven by great technology and amazing people. We're now tapping into the unlimited potential of AI to define the next era of computing, where our GPUs power computers, robots, and selfdriving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll work in a diverse, supportive environment where people are encouraged to do their best work and grow their careers. We offer a preference for hybrid work while remaining open to remote arrangements, giving you flexibility in how you do your best work.
Come join the team and see how you can make a lasting impact on the world. The DGX Cloud organization at NVIDIA brings together cuttingedge hardware and software innovation to deliver industryleading accelerated computing for the world's most ambitious AI workloads. We are a group of forwardthinking engineers tackling some of the globe's toughest challenges, pushing progress, and positively affecting millions of lives. We're searching for a Senior Systems Software Engineer with deep expertise in distributed systems, Kubernetes, containers, and systems performance and scalability. The ideal candidate brings broad, handson experience across the stack, including GPU operators, device plugins, distributed inference serving, and major cloud platforms. You'll own hard technical problems at large scale and help shape how AI infrastructure runs in production. In this key role, you will focus on scaling AI infrastructure while minimizing total cost of ownership, reducing cost per token and enabling future AI innovation and AI factories. Are you ready to be impactful?

What you'll be doing:

• Lead endtoend performance and scalability analysis across the Kubernetesbased accelerated runtime stack (control and data planes), including NVIDIA components such as GPU Operator, Network Operator, node-feature-discovery, topograph, dra-driver-nvidia-gpu, and nvsentinel, tracking issues from orchestration down to the metal.

• Design and contribute upstream architectural changes to the Kubernetes control plane and related projects to enable reliable operation at hyperscale cluster sizes, doing in the open what today's hyperscalers typically do privately.

• Improve container startup and coldstart latency to enable smooth, lowlatency inference scaling on Kubernetes across thousands of GPU nodes, ensuring the AI runtime stack scales without creating API server pressure or operational fragility.

• Assess, improve, and contribute to opensource projects that make Kubernetes an outstanding platform for AI workloads (for example, Grove and gateway-apiinferenceextension), composing their architectures with scalability, resilience, and multinode training/inference in mind.

• Advance scalability and performance of confidential containers (CoCo) on Kubernetes so encrypted inference workloads meet stringent efficiency and latency requirements in production.

• Use DSX and related largescale simulation infrastructure to model full AIfactory deployments and validate scalability across thousands of simulated GPUs, catching failures that emerge only at scale before hardware arrives.

• Collaborate with AI researchers, developers, customers, and upstream communities to design automated, atscale workload tests (including replay of production agent traces), build monitoring/analysis tooling, and integrate continuous performance and scale testing into modern CI/CD workflows.

• Document methods and results clearly and present findings internally and at industry events (for example, KubeCon, GTC), while actively engaging with upstream groups (Kubernetes SIG Scalability, CNCF, and NVIDIA OSS communities) to influence and validate AI workload performance and scalability directions.

What we need to see:

• Bachelor's or Master's degree in Engineering or equivalent experience, ideally inElectrical, Computer Engineering, or Computer Science

• 5+ years of experience in computer architecture, networking, storage systems, and acceleratorbased platforms

• Expertise in Kubernetes and familiarity with the broader CNCF ecosystem

• Deep experience with largescale, parallel, distributed accelerator systems and performance optimization of AI workloads

• Experience with performance modeling and benchmarking for largescale systems

• Proficiency in Golang and/or Python

• Strong familiarity with the NVIDIA software stack across training and inference

• Expertise with at least one major public cloud provider (for example, AWS, Azure, GCP, or OCI)

Ways to stand out from the crowd:

• Strong operational experience with any one of the Kubernetes distributions

• Prior experience scaling Kubernetes clusters to ultra-large node and object counts

• Demonstrated history of working in the open-source community

• Excellent communication and interpersonal abilities

• PhD or equivalent experience in relevant areas

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This posting is for an existing vacancy.

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