Job description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation fueled by great technology—and amazing people. The GPU, our invention, serves as the visual cortex of modern computers and sits at the heart of our products and services. Every one of those GPUs starts life inside our silicon design environment, and that environment runs on the platform this team builds. We're looking for a Senior SRE to own and scale the Kubernetes platform that assists NVIDIA's chip design (EDA) workloads. This is not a generic container-platform role. You'll be running Kubernetes at the edges of what it was developed for: orchestrating enormous stateful jobs, license-constrained EDA tooling, latency-sensitive access to high-performance shared storage, and tenants whose tapeout schedules do not move. The clusters you run sit alongside some of the largest EDA compute farms in the industry, spanning multiple on-prem datacenters and multiple public clouds. When these clusters degrade, chip schedules feel it - which is exactly what makes the reliability work exciting. What you'll be doing: • Own Kubernetes clusters end-to-end - plan, deliver, upgrade, day-2 operations, and decommission - across on-prem datacenters and managed cloud Kubernetes (e.g. EKS, GKE, RKE, Red Hat OpenShift, etc.). • Define and implement the multi-cluster lifecycle strategy: version skew policy, patch cadence, node image pipelines, controlled rollouts, and safe upgrade mechanics for clusters that can't simply be drained on demand. • Treat on-prem and CSP Kubernetes as one platform with consistent APIs, policy, and observability - not as two disconnected worlds. • Drive everything through Infrastructure-as-Code, GitOps, and declarative config; manual cluster changes should be an anomaly worth investigating. • Design for failure: explicit fault domains, blast-radius containment between clusters and tenants, progressive delivery, and disciplined change control. • Build end-to-end observability (metrics, logs, traces, events) and push toward data-driven and auto-healing operations that materially reduce human intervention. • Participate in on-call, lead incident response, drive root cause analysis, and publish high-quality RCAs that actually change the system. • Partner with EDA engineering, storage, and network teams to land changes cleanly in a fast-moving environment. • Engage and partner with data center operations to help forecast, design and implement the accurate on-prem and cloud deployments to meet architectural needs. • Interface directly with internal EDA tenants/engineering teams to design and implement operational improvements to increase productivity and provide faster and more reliable outcomes as well as partner with the infrastructure services team to help architect the supporting capabilities for intelligent workload routing, observability, dynamic scaling and resource governance. What we need to see: • Bachelor's degree in Computer Science or related technical field (or equivalent experience) with 8+ years building and supporting critical production services. • Deep, hands-on Kubernetes operations experience - you've run it, not just consumed it. Comfortable debugging etcd, kubelet, CNI, CSI, scheduler behavior, API server load, container runtime, and control-plane degradation under real production pressure. • Production experience with CSP-managed Kubernetes — including its upgrade model, networking and IAM integration, node group lifecycle, and where the managed abstraction stops helping you. • Production experience operating self-managed Kubernetes on-premises across multiple datacenters — cluster bring-up, bare-metal and GPU node lifecycle, multi-site topology, and the failure modes that only show up when you own the whole stack. • Strong command of CI/CD and Infrastructure-as-Code (Terraform, Ansible, Helm, Kustomize, Argo CD/Flux or equivalents). • Demonstrated experience building large-scale infrastructure platforms for automated host lifecycle management, fleet reliability and auto-healing, end-to-end observability, or data-driven operations (AIOps/ML-driven signals) that measurably reduced toil. • Proficiency with monitoring, metrics,, and log collection tooling (Prometheus, Grafana, OpenTelemetry, Loki/ELK, containerd, etc.). • 5+ years of coding/scripting focusing on automation and operational management at scale in at least two languages such as Go, Python, Perl, or Ruby - with Go a strong plus for controller and operator work. • Track record mentoring engineers and influencing technical direction through design reviews, architecture documents, and strong partnership with team members and leadership. • Creative problem solver with excellent debugging instincts and strong written communication. Ways to stand out from the crowd: • Familiarity with EDA/silicon-design workflows, license management, or other license-constrained compute environments. You've written or built Kubernetes controllers, operators, admission webhooks, scheduler plugins, or device/CSI plugins that ran in production. • Experience running stateful, storage-heavy, or GPU-accelerated workloads on Kubernetes at scale. • Published technical write-ups or talks (conference presentations, meetups, engineering blogs) that deep-dive into real-world reliability, observability, or large-scale Kubernetes/HPC problems. • Maintainer or co-maintainer responsibilities for an open source component used in production at scale - operators, exporters, controllers, plugins, or SDKs. We have some of the most forward-thinking and hard-working people in the world working for us and, due to unprecedented growth, our engineering teams are growing fast. If you're a creative and autonomous engineer with real passion for technology, we want to hear from you. NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. Our invention serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is seeking exceptional individuals like you to help us drive the next wave of artificial intelligence. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 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 October 15, 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.

Requirements

Must-haves

Own Kubernetes clusters end-to-end - plan, deliver, upgrade, day-2 operations, and decommission - across on-prem datacenters and managed cloud Kubernetes (e.g. EKS, GKE, RKE, Red Hat OpenShift, etc.).
Define and implement the multi-cluster lifecycle strategy: version skew policy, patch cadence, node image pipelines, controlled rollouts, and safe upgrade mechanics for clusters that can't simply be drained on demand.
Treat on-prem and CSP Kubernetes as one platform with consistent APIs, policy, and observability - not as two disconnected worlds.
Drive everything through Infrastructure-as-Code, GitOps, and declarative config; manual cluster changes should be an anomaly worth investigating.
Design for failure: explicit fault domains, blast-radius containment between clusters and tenants, progressive delivery, and disciplined change control.
Build end-to-end observability (metrics, logs, traces, events) and push toward data-driven and auto-healing operations that materially reduce human intervention.
Participate in on-call, lead incident response, drive root cause analysis, and publish high-quality RCAs that actually change the system.
Partner with EDA engineering, storage, and network teams to land changes cleanly in a fast-moving environment.
Engage and partner with data center operations to help forecast, design and implement the accurate on-prem and cloud deployments to meet architectural needs.
Bachelor's degree in Computer Science or related technical field (or equivalent experience) with 8+ years building and supporting critical production services.
Production experience with CSP-managed Kubernetes — including its upgrade model, networking and IAM integration, node group lifecycle, and where the managed abstraction stops helping you.
Strong command of CI/CD and Infrastructure-as-Code (Terraform, Ansible, Helm, Kustomize, Argo CD/Flux or equivalents).
Proficiency with monitoring, metrics,, and log collection tooling (Prometheus, Grafana, OpenTelemetry, Loki/ELK, containerd, etc.).
5+ years of coding/scripting focusing on automation and operational management at scale in at least two languages such as Go, Python, Perl, or Ruby - with Go a strong plus for controller and operator work.
Track record mentoring engineers and influencing technical direction through design reviews, architecture documents, and strong partnership with team members and leadership.
Creative problem solver with excellent debugging instincts and strong written communication.
Experience running stateful, storage-heavy, or GPU-accelerated workloads on Kubernetes at scale.
Published technical write-ups or talks (conference presentations, meetups, engineering blogs) that deep-dive into real-world reliability, observability, or large-scale Kubernetes/HPC problems.
Maintainer or co-maintainer responsibilities for an open source component used in production at scale - operators, exporters, controllers, plugins, or SDKs.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until October 15, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

kubernetesapici/cdmlgopythonai

Platform: eightfold
Effort: medium