Graphcore is a globally recognized leader in Artificial Intelligence computing systems. The company designs advanced semiconductors and data centre hardware that provide the specialized processing power needed to drive AI innovation, while delivering the efficiency required to support its broader adoption.   

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. We are opening a new AI Engineering Campus in Austin, which will play a central role in Graphcore's work building the future of AI computing. 

About the Role

We are seeking a Storage Architect to design, evaluate, and optimize the high-performance storage architectures powering our AI data centers. In AI training and inference, keeping GPUs fed with data is one of the most critical challenges; even a slight I/O bottleneck can lead to massive inefficiencies. In this role, you will be our resident expert on solid-state storage, focusing heavily on NVMe SSDs, PCIe topologies, and the Linux storage stack. You will ensure our local and distributed storage tiers deliver the microsecond predictable latency and massive throughput required for large language model (LLM) checkpointing, massive datasets, and high-speed data loading pipelines.

RESPONSIBILITIES

• Hardware Qualification: Lead the evaluation, qualification, and lifecycle management of NVMe SSDs (e.g., E1.S, E3.S, U.2/U.3 form factors) from major vendors for use in high-density AI servers and AI Clusters.
• Architecture & Topology: Design and optimize local server storage topologies, managing PCIe lane distribution, minimizing NUMA node crossings, and ensuring optimal data paths between NVMe drives, CPUs, and GPUs.
• Performance Tuning: Conduct rigorous performance profiling and tuning of the Linux kernel storage stack, block layer, and file systems to maximize IOPS and bandwidth while minimizing tail latency (i.e., 99.99%).
• AI Workload Optimization: Optimize storage configurations specifically for AI workloads, including tuning for GPU direct storage to enable direct memory access between NVMe storage and GPU memory, bypassing the CPU.
• Telemetry & Automation: Drive telemetry strategy for the storage subsystem including SSD health monitoring (i.e., wear leveling, DWPD, thermals) and latency anomaly detection. Provide requirements and technical guidance to the automation team building test framework to characterize the storage subsystem in our AI Platforms. Additionally, manage firmware rollouts at scale.
• Troubleshooting: Act as the highest point of escalation for complex, storage-related performance degradation or hardware failures, utilizing tools like nvme-cli, fio, and PCIe analyzers.
• Vendor engagement:  Responsible for working closely with storage vendors, supply chain teams, and our Systems engineering team in the selection and proper deployment of storage components into our AI server fleet. 
• “Futures”:  As a storage architect you will be responsible for scoping the industry for new storage technologies that will give us an advantage on future AI Server designs.
• Storage Control and Dataplane Definition: Storage expert in charge of the definition and architecture of our external storage Data plane and Control plane solutions using leading vendor solutions such as Weka, VAST, Pure Storage, and others. 

REQUIREMENTS

• Bachelor’s, master’s degree or equivalent experience in Information Technology, Computer Science, Computer engineering, or a related field.
• 12+ years of hands-on experience in storage engineering and architecture and 5+ years supporting storage solutions on AI or Hyperscale Datacenters.
• Deep NVMe/PCIe Knowledge: Expert-level understanding of the NVMe protocol, PCIe architecture (Gen 5/Gen 6), NAND flash characteristics, and SSD controller behavior (garbage collection, wear leveling, write amplification).
• Linux Storage Stack: Deep operational knowledge of Linux internals, specifically the block layer, I/O schedulers, direct I/O, and modern local file systems (XFS, ext4, ZFS).
• Performance Profiling: Extensive hands-on experience with benchmarking and profiling tools such as fio, blktrace, iostat, and perf to simulate AI workloads and isolate bottlenecks.
• Scripting: Strong programming skills in Python, JSON, Bash or related scripting languages for building validation scripts, automation pipelines, and interacting with REST APIs.
• Root Cause Analysis: Proven track record of debugging complex hardware/software interactions, including kernel panics, PCIe bus errors, and SSD firmware bugs.
• Telemetry: Point of contact responsible for defining the telemetry sensors and rules to activate alerts in our Datacenter fleet monitoring frameworks.

DIFFERENTIATORS

• GPU Direct Storage (GDS): Experience implementing, testing, and troubleshooting GDS or similar technology for direct GPU-to-storage communication.
• NVMe over Fabrics (NVMe-oF): Experience designing or managing NVMe-oF (RoCEv2 or TCP) deployments to disaggregate storage without sacrificing local-NVMe performance.
• Storage Software Development: Experience with the Storage Performance Development Kit (SPDK) or developing user-space storage applications.
• Distributed AI File Systems: Familiarity with high-performance parallel file systems commonly used in AI/HPC clusters (e.g., Weka, VAST Data, Lustre, DAOS, or DDN).
• Emerging Tech: Knowledge of Compute Express Link (CXL) and its implications for future memory and storage tiering in AI servers.

We welcome people of different backgrounds and experiences and are committed to building an inclusive work environment that makes Graphcore a great home for everyone. We are an equal opportunity employer and want to build a work environment where everyone is happy, productive and respectful so they can do their best work. If you have a disability or additional need that requires accommodation, just let us know.

Graphcore is a globally recognized leader in Artificial Intelligence computing systems. The company designs advanced semiconductors and data centre hardware that provide the specialized processing power needed to drive AI innovation, while delivering the efficiency required to support its broader adoption.   

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. We are opening a new AI Engineering Campus in Austin, which will play a central role in Graphcore's work building the future of AI computing. 

RESPONSIBILITIES

• Responsible for the maintaining all the network fabrics and network topologies operating at peak performance on Graphcore Data centers housing AI server platforms.
• Collaborate closely with our Network architecture team through all phases of the design and development lifecycle — from concept to deployment — ensuring timely, high-quality, and stable high-speed network fabrics.
• Primary support of deployed internal fabrics in our AI Datacenters.
• Direct escalation for Hardware, Link and, Performance issues on network fabrics.
• Escalation support for customer network fabrics (aligned to reference arch.)
• Triage day-to-day network topologies (scale-up, scale-out, and front-end networks) issues of Fabrics deployed in Graphcore Datacenters.

REQUIREMENTS

• Bachelor’s, master’s degree or equivalent experience in Network Engineering/Security, Information Technology, Computer Science, Computer engineering, or a related field.
• 9+ years of hands-on experience in network engineering and 3+ years supporting networking on AI or Hyperscale Datacenters.
• Knowledge of Remote Direct Memory Access (RDMA) and its implementations, specifically ROCEv2 or InfiniBand
• Knowledge of Lossless network – Experience with congestion control and lossless Ethernet technologies including PDF, ECN and DCQCN.
• Versed in High-Bandwidth network infrastructure including experience with operating 400G and 800G network fabrics and tackling challenges at the physical layer for networks operating at these speeds. This includes familiarity with specific optical transceivers.
• Experience sustaining non-blocking, multi-tier CLOS networks (i.e., Spine / Leaf / Super-Spine) that are optimized for high-density GPU clusters.
• Extensive troubleshooting experience for data center routing protocols such as BGP and OSPF as well as troubleshooting of VXLAN implementations.
• Cross-Functional collaboration- The candidate must excel at working closely with network architects, systems hardware engineers, AI software engineers, and Datacenter infrastructure teams to optimize the full stack from the application layer down to the Network infrastructure that includes controllers (NICs, SmartNICs, DPUs), network switches, and cabling and optical transceiver operation in our Datacenter.

DIFFERENTIATORS

• Deep hands-on experience with NetDevOps to automate tasks using tools such as Terraform or Ansible to manage network configurations and state.
• Possess strong scripting skills such as Python, Go, or JSON to build custom automation, interact with APIs, and develop internal network tools.
• Deep hands-on experience with modern, high-performance hardware from network switch and NIC providers such as Broadcom, Arista, Cisco, or Pensando.
• Knowledgeable in Advanced Network Telemetry, beyond traditional SNMP. Knowledgeable in telemetry protocols such as gNMI, Sflow, Netflow, Redfish, and Prometheus to characterize and detect transient congestion events, micro-bursts in the network fabric or defective network hardware.

We welcome people of different backgrounds and experiences and are committed to building an inclusive work environment that makes Graphcore a great home for everyone. We are an equal opportunity employer and want to build a work environment where everyone is happy, productive and respectful so they can do their best work. If you have a disability or additional need that requires accommodation, just let us know.

pproach to interview and encourage you to chat to us if you require any reasonable adjustments. 

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.
  • Hands‑on experience with modern merchant silicon networking platforms and NOS platforms such as Arista EOS, Cisco NX‑OS, or SONiC.
  • Experience deploying high‑speed network technologies including 400G/800G optics and large‑scale fabric architectures.
  • Proficiency in automation and scripting languages such as Python, Go, Bash, or similar tools.
  • Strong collaboration and communication skills across cross‑functional engineering teams.
  
  

Desirable

• Experience operating large‑scale AI or GPU clusters.
  • Familiarity with network telemetry frameworks and streaming analytics.
  • Experience implementing NetDevOps workflows and infrastructure automation pipelines.
  • Experience influencing vendor roadmaps or evaluating next‑generation networking technologies.