HPC Network Architect for AI Clusters

CommonAI C.I.C.

Cambridge

Hybrid

GBP 70,000 - 105,000

Full time

12 days ago

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Benefits offered by this job

Competitive salary + pension
Professional development opportunities
Networking with tech/academic circles
Vibrant Cambridge office near train站
Collaborative and supportive team

Job summary

CommonAI CIC is seeking a seasoned network design engineer to architect and deliver scalable HPC/AI cluster networks. You will own end-to-end cabling, topology, switch placement иand BOM planning for lab clusters in the UK.

You will collaborate with project managers, installers and engineers to ensure precise drawings, robust specifications and clear as-built documentation for future reuse and scalability.

Qualifications

  • Experience designing networks for HPC or AI clusters from specification to installation.
  • Thorough understanding of out-of-band, frontend and accelerator fabrics networking.
  • Practical knowledge of InfiniBand, RoCE or similar lossless fabrics, including fat-tree/topologies.
  • Strong knowledge of data centre fibre optics: OS2 vs OM4/OM5, connectors and losses.

Responsibilities

  • Design the physical network for new clusters: topology, switch placement, BOM, cabling schedules.
  • Specify fibre/copper infrastructure in detail, choosing transceivers and connectors.
  • Plan changes to existing clusters to allow re-cabling with minimal disruption.
  • Act as technical authority for suppliers and installers; review quotations and inspect work.

Skills

HPC networks
AI clusters
InfiniBand / RoCE
Data centre fibre optics
Clear communicator

Tools

NetBox

Job description

CommonAI CIC is seeking a seasoned network design engineer to architect and deliver scalable HPC/AI cluster networks. You will own end-to-end cabling, topology, switch placement иand BOM planning for lab clusters in the UK.

You will collaborate with project managers, installers and engineers to ensure precise drawings, robust specifications and clear as-built documentation for future reuse and scalability.

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