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FluidStack

Builds and runs GPU clusters for AI labs

Network Engineer - Design & Engineering

Network Engineering & AdministrationConfirmed live in the last 24 hours
Full-timeNew York, NY, USAHybrid

About the Job

Requirements

  • Experience designing data center network fabrics from requirements through deployment and explaining the tradeoffs behind design decisions.
  • Deep Layer 1 to Layer 3 expertise, including CLOS/fat-tree topologies, BGP, EVPN/VXLAN, and their underlying fundamentals.
  • Experience designing lossless RDMA (RoCEv2) fabrics and understanding congestion management at training scale.
  • Ability to reason from first principles through novel design challenges rather than relying on one reference architecture.
  • Ability to partner across Hardware, DC Operations, ICT, Software, and Validation to ensure designs are buildable and operationally sound.

Responsibilities

  • Own the network design lifecycle from customer requirements, including GPU shape, workload, scale, and tenancy, through deployable, validated architectures for AI training and inference.
  • Produce topology designs, IP/addressing schemes, routing policy, and fabric configuration specifications across front-end, back-end GPU-to-GPU training, and storage networks.
  • Adapt architectures to different GPU platforms, including NVIDIA, AMD, and custom accelerators, form factors, and workload profiles, accounting for rack layout, power envelope, and cabling approach.
  • Translate logical designs into physical designs, including rack elevations, power constraints, structured cabling and fiber budgets, pathway routing, and airflow considerations that affect equipment placement.
  • Design lossless Ethernet fabrics for RDMA (RoCEv2), including PFC, ECN tuning, traffic classes, and congestion management, while reasoning about ECMP and collective-communication patterns in distributed training.
  • Produce HLDs, LLDs, cutsheets, BOMs, cabling matrices, and design decision records, and lead design reviews and reusable reference architectures.

Desired Qualifications

  • Experience with source-of-truth-driven design generation.
  • Experience integrating multi-vendor GPU platforms.
  • Experience with large clusters of 100,000 or more accelerators.

About the Company

FluidStack

Builds and runs GPU clusters for AI labs

Fluidstack provides large clusters of GPU servers to AI labs training advanced models, and it builds custom data centers for customers that outgrow existing facilities. In 2025, Anthropic chose Fluidstack to build data centers in Texas and New York as part of a $50 billion investment in American AI infrastructure. Labs pay for dedicated clusters of Nvidia GPUs, sometimes tens of thousands of chips, housed in partner data centers around the world. They reach the clusters through a web console or API, while Fluidstack runs the hardware, networking, and day-to-day operations. Fluidstack was founded in London in 2017 and has its headquarters in New York.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$12.4B
Headquarters
New York City, New York
Founded
2017

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