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

Senior Systems Software Engineer – DGX Cloud

Joining NVIDIA’s DGX Cloud organization, you will help shape AI infrastructure for large‑scale, cost‑effective deployments. This role focuses on performance, scalability, and cost optimization of AI workloads on Kubernetes‑based runtimes.

• Lead end‑to‑end performance and scalability analysis across the Kubernetes‑based accelerated runtime stack (control and data planes), including 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.
• Improve container startup and cold‑start latency to enable smooth, low‑latency inference scaling on Kubernetes across thousands of GPU nodes.
• Assess, improve, and contribute to open‑source projects that make Kubernetes an outstanding platform for AI workloads (e.g., Grove and gateway‑api‑inference‑extension).
• 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, 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 (e.g., Kube Con, GTC), while engaging with upstream groups (Kubernetes SIG Scalability, CNCF, and NVIDIA OSS communities) to influence AI workload performance and scalability directions.

• 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.
• Expertise with at least one major public cloud provider (e.g., AWS, Azure, GCP, or OCI).

• Base salary range: 184,000
  
  USD – 287,500
  
  USD (Level4) or 224,000
  
  USD – 356,500
  
  USD (Level5).
• Equity and benefits package.
• Hybrid work preference with remote options.

NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.