Senior Systems Software Engineer, Accelerated Kubernetes Performance and Scale - DGX Cloud

What You'll Be Doing

Lead end-to-end performance and scalability analysis across the Kubernetes-based 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 cold-start latency to enable smooth, low-latency 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 open-source projects that make Kubernetes an outstanding platform for AI workloads (for example, Grove and gateway-api-inference-extension), composing their architectures with scalability, resilience, and multi-node 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 large-scale simulation infrastructure to model full AI-factory 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, at-scale 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 in Electrical, Computer Engineering, or Computer Science
• 8+ years of experience in computer architecture, networking, storage systems, and accelerator-based platforms
• Expertise in Kubernetes and familiarity with the broader CNCF ecosystem
• Deep experience with large-scale, parallel, distributed accelerator systems and performance optimization of AI workloads
• Experience with performance modeling and benchmarking for large-scale systems
• Proficiency in Golang and/or Python
• Strong familiarity with the NVIDIA software stack across training and inference
• Experience 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