Senior Network Engineer – GPU Cluster Networking

• Architect, deploy, operate, and continuously improve high-performance backend networks for large-scale AMD Instinct GPU clusters.
• Design network fabrics capable of supporting AI and HPC environments ranging from individual GPU racks to clusters containing 10,000 or more GPUs.
• Own the backend network architecture from the GPU server and network interface card through the leaf-spine switching fabric.
• Design and optimize high-speed Ethernet fabrics using RoCEv2 and 100/200/400 GbE technologies.
• Develop scalable network topologies, including leaf-spine, Clos, fat-tree, rail-optimized, multi-plane, and non-blocking fabric architectures.
• Perform network topology modeling, oversubscription analysis, traffic-flow analysis, bandwidth planning, port-capacity planning, failure-domain analysis, and long-term growth forecasting.
• Configure, tune, validate, and troubleshoot lossless or near-lossless RoCEv2 environments, including PFC, ECN, DCQCN, QoS, ECMP, Switch buffer and queue management, DSCP and priority mappings
• Design and operate routing and switching environments using technologies such as BGP, ECMP, VLAN, VRF, EVPN, and VXLAN.
• Optimize end-to-end communication performance across GPUs, NICs, switches, CPUs, PCIe devices, storage systems, and the Linux networking stack.
• Lead production incident response, root-cause analysis, corrective actions, and preventive engineering improvements for GPU cluster networks.
• Plan and execute network expansions, cluster scale-outs, switch replacements, capacity upgrades, and fabric migrations

• Significant experience designing, deploying, and operating production data center networks for AI, GPU, HPC, cloud, or other large-scale distributed computing environments.
• Experience designing, scaling, or operating backend network infrastructure for GPU clusters containing approximately 10,000 or more GPUs, or similarly sized hyperscale compute environments.
• Deep knowledge of data center networking fundamentals; Routing and switching, VLANs and subnetting, BGP and ECMP, Quality of Service, MTU configuration, Switch buffering, Network segmentation
• Strong hands-on experience with RDMA and RoCEv2 in production environments.
• Demonstrated experience configuring, tuning, and troubleshooting PFC, ECN, DCQCN, QoS, switch buffers, NIC queues, RDMA traffic classes, and lossless or near-lossless Ethernet.
• Strong understanding of leaf-spine, Clos, fat-tree, rail-optimized, and multi-plane network architectures.
• Experience with network routing technologies such as BGP and ECMP and overlay technologies such as EVPN and VXLAN.
• Strong understanding of GPU cluster topology, including GPU-to-GPU, GPU-to-NIC, CPU-to-NIC, PCIe, NUMA, and network locality.
• Experience building monitoring and observability solutions using Prometheus, Grafana, streaming telemetry, gNMI, SNMP, sFlow, or equivalent platforms.
• Experience with Juniper data center switching platforms and Junos OS , including configuration and troubleshooting
• Experience with AMD Instinct accelerators, ROCm, RCCL, and AMD GPU software environments.
• Experience with AMD Pensando AI NICs, SmartNICs, DPUs, or other AMD Pensando networking technologies.
• Strong hands-on experience with Juniper data center switching platforms and Junos OS, including configuration and troubleshooting
• Experience designing backend networks specifically for large language model training and other communication-intensive distributed AI workloads.
• Experience with Ethernet fabric technologies such as BGP, EVPN, VXLAN, and modern leaf-spine data center architectures.

• Bachelor’s or Master’s degree in Computer Engineering, or a related field, or equivalent practical experience.

Benefits offered are described: AMD benefits at a glance .

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This posting is for an existing vacancy.