NVIDIA is a pioneer in accelerated computing, known for inventing the GPU and driving breakthroughs in gaming, computer graphics, high-performance computing, and artificial intelligence. Our technology powers everything from generative AI to autonomous systems, and we continue to shape the future of computing through innovation and collaboration. Within this mission, our team, Managed AI Research Superclusters (MARS), builds and scales the infrastructure, platforms, and tools that enable researchers and engineers to develop the next generation of AI/ML systems. By joining us, you'll help build solutions that power some of the most sophisticated computing workloads globally.
We are looking for a senior networking engineer to lead the network architecture of our GPU superclusters. Our platform runs frontier model training across tens of thousands of GPUs on multiple clouds, and we are scaling it toward clusters of ten thousand nodes and beyond. At that size, networking stops being a configuration exercise and becomes the hardest engineering problem on the platform — and it is currently one of the areas where we most need depth.
What you'll be doing:
You will be the technical owner of how our clusters communicate internally and externally. This includes the CNI data plane, the overlay mesh, and the nodes connecting clusters across regions and providers.
Own and evolve the Kubernetes networking architecture for GPU clusters running at multi-thousand-node scale
Design, operate and scale the overlay network - CNI, mesh and VPN topologies (Tailscale, WireGuard), and the gateways that connect control and data planes
Design, operate and scale the L7 gateways/load balancers/tunnels (Envoy, Cloudflare)
Find and eliminate scale ceilings: packet loss under load, control-plane saturation, IP address management exhaustion, and the failure modes that only appear above a few thousand nodes
Build the scale-test environments and validation suites that let us catch networking regressions before they reach production, rather than during a training run
Diagnose hard, ambiguous problems across the stack - where a symptom in Slurm or a training job traces back to a mark collision, a stale route, or a saturated tunnel
Partner with cloud and neocloud providers on network topology, requirements and capabilities as we bring up new clusters
Provide senior technical judgement to a distributed team, and depth in the Custer Networking domain.
What we need to see:
BS/MS in Computer Science, Electrical Engineering or a related field, or equivalent experience
6+ years of professional experience in systems, network or infrastructure software engineering
Deep command of Kubernetes networking architecture and CNI standards, with production experience operating Calico strongly preferred
Proficiency designing and maintaining modern mesh and VPN networking topologies - Tailscale, WireGuard or equivalent
Strong Linux networking fundamentals: routing, netfilter and iptables/nftables, packet marking, network namespaces, and how these interact with container runtimes
Demonstrated ability to debug distributed network problems at scale - packet capture, tracing, and correlating behaviour across many hosts to find a single root cause
Proficiency in Go, Python, C or a comparable systems language
Clear written and verbal communication, and the ability to work effectively with engineers across multiple time zones
Ways to stand out from the crowd:
Direct experience architecting and operating massive-scale Kubernetes topologies across thousands of concurrent nodes
Experience with high-performance fabrics in AI or HPC environments - InfiniBand, RoCE, or RDMA over converged networks
Upstream contributions to Calico, Cilium, Tailscale, or Kubernetes networking SIGs
Experience operating networking across multiple public clouds and on-premises environments simultaneously
Familiarity with Slurm or other HPC schedulers running on Kubernetes
You will also be eligible for equity and benefits.
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.Skills Required
- Bachelor's or master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience
- 6+ years of professional experience in systems, network, or infrastructure software engineering
- Deep knowledge of Kubernetes networking architecture and CNI standards
- Production experience operating Calico
- Experience designing and maintaining mesh and VPN networking topologies using Tailscale, WireGuard, or equivalent technologies
- Strong Linux networking fundamentals, including routing, netfilter, iptables/nftables, packet marking, network namespaces, and container runtimes
- Ability to debug distributed network problems at scale using packet capture, tracing, and multi-host analysis
- Proficiency in Go, Python, C, or a comparable systems programming language
- Clear written and verbal communication skills and ability to collaborate across multiple time zones
- Experience architecting and operating Kubernetes topologies across thousands of concurrent nodes
- Experience with InfiniBand, RoCE, or RDMA in AI or HPC environments
- Upstream contributions to Calico, Cilium, Tailscale, or Kubernetes networking SIGs
- Experience operating networking across multiple public clouds and on-premises environments
- Familiarity with Slurm or other HPC schedulers running on Kubernetes
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.
-
Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
-
Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
-
Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
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.”
