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
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
#LI-Hybrid
- Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
- 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) 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.
