The Role
Designs, develops, and operates large-scale software-defined networking for NVIDIA AI Cloud environments. Responsibilities include building OVS/OVN control and data plane software, Kubernetes networking services, observability systems, CI/CD pipelines, GitOps infrastructure, and production reliability tooling. The role requires expertise in C, Go, distributed systems, cloud networking, Kubernetes CNIs, infrastructure automation, secure service development, and datacenter networking.
Summary Generated by Built In
We are looking for a Principal System Software Engineer, Software Defined Networking to design, build, and operate highly performant and scalable SDN solutions for NVIDIA's AI Clouds hosting GPU-accelerated workloads — including hyperscale multi-node training, inference, cloud gaming, and cloud functions. This role spans the full lifecycle of our SDN stack — from designing and developing new control and data plane software to ensuring operational excellence in production through reliability engineering, CI/CD, observability, and incident response.
What you'll be doing:
- Design and develop next-generation multi-tenant cloud SDN control and data plane software (OVS, OVN, OpenFlow)
- Build Infrastructure-as-a-Service virtual network orchestration and services using gRPC and REST to support tenant workload security and performance SLAs for BMaaS, VMaaS, and Kubernetes
- Drive upstream contributions to OVN-Kubernetes and related open-source projects
- Develop software for network observability — monitoring, telemetry, intelligent metering, and performance analysis
- Operate and support OVS-OVN based SDN solutions in large-scale NVIDIA AI Cloud environments
- Own end-to-end observability for the SDN stack — build and maintain monitoring, alerting, distributed tracing, and dashboarding to ensure real-time insight into network health, performance, and tenant SLAs
- Design, enhance, and maintain CI/CD pipelines (GitLab) across Linux host networking, OVS, OVN, and Kubernetes CNIs
- Implement GitOps approaches or related experience for secure, seamless integration with cloud infrastructure
- Collaborate with SRE, DevOps, and network engineering teams on production readiness and operational tooling
What we need to see:
- BS/MS in Computer Science or related technical field, or a comparable blend of education and relevant experience
- 15+ years of proven experience in software development for large-scale distributed environments
- Expert-level knowledge of OVN, OVS, OpenFlow, and modern network protocols
- Strong programming skills in C and Go; advanced scripting in Bash and Python
- Deep knowledge of Kubernetes, practical experience deploying and supporting CNIs (OVN-Kubernetes)
- Hands-on experience with Infrastructure-as-Code and deployment tools (Ansible, Terraform, ArgoCD, Flux)
- Experience designing and operating complex, multi-stage CI/CD pipelines
- Hands-on experience developing secure, high-performance services using gRPC and REST with TLS and strong authentication
- Strong knowledge of datacenter routing, switching, and Linux host/VM networking
Ways to stand out from the crowd:
- Contributions to open-source projects (especially OVS, OVN, OVN-Kubernetes, or other Kubernetes networking projects)
- Experience with hardware acceleration (GPU, DPU or equivalent experience) for networking
- Practical experience with major cloud providers (AWS, Azure, GCP) and hybrid/multi-cloud deployments
- SRE/DevOps top-level expertise — on-call, incident management, operations focused on service reliability targets, production ownership
- Experience with observability platforms and tools (Prometheus, Grafana, Jaeger, OpenTelemetry, ELK)
Skills Required
- Bachelor’s or master’s degree in Computer Science or a related technical field, or comparable education and experience
- 15+ years of software development experience in large-scale distributed environments
- Expert knowledge of OVN, OVS, OpenFlow, and modern network protocols
- Strong programming skills in C and Go
- Advanced scripting skills in Bash and Python
- Deep Kubernetes knowledge and practical experience deploying and supporting CNIs, especially OVN-Kubernetes
- Hands-on Infrastructure-as-Code and deployment experience with Ansible, Terraform, ArgoCD, and Flux
- Experience designing and operating complex, multi-stage CI/CD pipelines
- Experience developing secure, high-performance gRPC and REST services with TLS and strong authentication
- Strong knowledge of datacenter routing, switching, and Linux host and virtual machine networking
- Open-source contributions, particularly to OVS, OVN, OVN-Kubernetes, or Kubernetes networking projects
- Experience with GPU, DPU, or equivalent hardware acceleration for networking
- Experience with AWS, Azure, GCP, and hybrid or multi-cloud deployments
- SRE or DevOps expertise, including on-call, incident management, service reliability targets, and production ownership
- Experience with Prometheus, Grafana, Jaeger, OpenTelemetry, or ELK
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
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The Company
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
