Senior Principal Network Engineer

Graphcore
Austin, Texas, United StatesPosted 21 March 2026

Tech Stack

Job Description

About us Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation. Job Summary We are seeking a Senior Principal Network Engineer to help design, deploy, and optimize next‑generation AI data center networks. AI training and inference workloads require extremely high bandwidth, deterministic low latency, and zero‑packet‑loss networking environments. In this role, you will partner closely with the Network Architecture Lead to design and scale high‑performance computing (HPC) network fabrics supporting GPU clusters. You will work across hardware, networking, and AI application layers to ensure Graphcore’s large‑scale AI infrastructure operates at peak performance. The ideal candidate brings deep experience operating hyperscale or HPC data center networks and has expertise in high‑speed Ethernet fabrics, RDMA technologies, advanced automation, and telemetry systems. The Team The Data Center Network Engineering team designs and operates the high‑performance network fabrics that power Graphcore’s AI compute platforms. The team collaborates closely with hardware engineering, AI researchers, and infrastructure teams to build scalable networking environments optimized for distributed training and inference workloads. Engineers work on pioneering technologies including high‑speed Ethernet fabrics, lossless networking, RDMA transport, and large‑scale automation frameworks to support next‑generation AI clusters. Responsibilities and Duties Assist in defining ultra‑high‑bandwidth, non‑blocking AI network fabrics (Clos spine‑leaf‑super‑spine architectures) for large‑scale distributed AI workloads. • Optimize performance of lossless Ethernet fabrics using congestion control mechanisms such as PFC, ECN, and DCQCN to support RDMA/RoCEv2 communication. • Lead initiatives to implement NetDevOps practices and develop automation for provisioning, configuration management, and network remediation. • Design and deploy high‑resolution telemetry pipelines to monitor network health, detect microbursts, and analyze congestion patterns. • Support modeling, deployment, configuration, and monitoring of data center network fabrics including scale‑out, scale‑up, and front‑end networks. • Collaborate cross‑functionally with hardware engineers, AI researchers, and data center operations teams to co‑design high‑performance infrastructure. • Provide technical leadership and mentorship to network engineers while establishing best practices and operational standards. • Contribute to the long‑term networking strategy and roadmap for Graphcore’s AI infrastructure. • Research and evaluate next‑generation high‑speed networking technologies and vendor solutions. Candidate Profile Essential BS or MS or equivalent experience in Computer Science, Electrical Engineering, Network Engineering, or related technical discipline. • 12+ years of progressive network engineering experience with at least 3 years in hyperscale, high‑density, or HPC data center environments. • Expert‑level knowledge of data center routing and switching protocols including BGP, OSPF, and EVPN‑VXLAN architectures. • Strong operational understanding of RDMA networking technologies such as RoCEv2 or InfiniBand. • Ha ... (truncated, view full listing at source)
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