Build and own automation for AI infrastructure networking, from provisioning NVIDIA switches and InfiniBand fabrics to managing network state, gateways, load balancing, and production-scale failure behavior.
Staff Networking Platform Engineer, Infrastructure
📜 Description
- Define how networking features are requested, provisioned, changed, observed, and removed.
- Automate tenant and node network lifecycles with Kubernetes-native tooling, including recovery from partial failures.
- Manage BGP, routing policy, VLANs, overlays, VIPs, load balancers, and public IPs through fabric APIs.
- Automate GPU networking and validate isolation and performance across NVL72, InfiniBand, RoCE, and GPUDirect.
- Keep IPAM, inventory, intended configuration, and applied network state in sync.
- Set segmentation and traffic policies with security engineering; enforce standards through GitOps and drift reconciliation.
🛠️ Requirements
- Deep networking: TCP/IP, BGP, routing policy, ECMP, segmentation, interconnects
- Production ownership of bare-metal leaf-spine data center networks with VLANs, VRFs and EVPN/VXLAN
- Kubernetes and Linux networking: CNI behavior, Cilium or similar, network policy
- Kubernetes controllers, operators or custom resources in Go, with GitOps and controlled rollouts
- Multitenant infrastructure or managed Kubernetes products
- Securing networks that run customer workloads
✨ Benefits
- Bonus folded into base compensation.
- Equity component.
Full job description
Location: Remote (US). Must be based in Dallas, San Francisco or New York City. Remote for now, with hybrid planned further down the road.
Pay:
L6 (Staff): $270,400–$365,800
L7 (Sr. Staff): $341,400–$461,800
Compensation is not an issue for the right person. The role pays nearly all cash: the bonus is folded into base, and there is an equity component on top.
A late-stage AI cloud company backed by NVIDIA, with a valuation in the billions, runs AI compute out of data centers around the world. It is now building a managed Kubernetes platform that turns large fleets of bare-metal GPU compute into dedicated, isolated clusters for customers running AI training and inference. This role owns networking across the whole platform, from Kubernetes cluster networking down through the data center fabric and GPU interconnect, and turns customer connectivity, isolation and performance needs into managed platform features.
What you'll do
Define how networking features are requested, provisioned, changed, observed and removed
Automate tenant and node network lifecycles with Kubernetes-native tooling, including safe recovery from partial failures
Manage BGP, routing policy, VLANs and overlays, plus VIPs, load balancers and public IPs, through fabric APIs
Automate GPU networking (NVL72, InfiniBand, RoCE, GPUDirect) and validate isolation and performance
Own IPAM and inventory integration so intended config, applied state and inventory stay in sync
Set segmentation and traffic policy with security engineering, and standards enforced through GitOps and drift reconciliation
Own network telemetry, troubleshooting and incident response
What you bring
Deep networking: TCP/IP, BGP, routing policy, ECMP, segmentation, interconnects
Production ownership of bare-metal leaf-spine data center networks with VLANs, VRFs and EVPN/VXLAN
Kubernetes and Linux networking: CNI behavior, Cilium or similar, network policy
Kubernetes controllers, operators or custom resources in Go, with GitOps and controlled rollouts
Multitenant infrastructure or managed Kubernetes products
Securing networks that run customer workloads
Nice to have: Netris, UFM or NetBox; NVIDIA AI networking (InfiniBand, Spectrum-X, RDMA, RoCE, congestion control); networking for large GPU clusters, including multi-site
Mural Group is recruiting for this role on behalf of a client. Apply here and a Mural recruiter will be in touch.
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