About Fluidstack

We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it.

We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI.

We hire people who care deeply about this problem space. If that is you, please apply!

About the Role

Fluidstack is seeking a Network Engineer, Design & Engineering to join our Network Engineering team. This is a design-ownership role: you will take customer requirements — GPU shape, workload profile, scale targets, tenancy model — and produce end-to-end network architectures that are deployable, validated, and optimized for AI training and inference workloads.

This is not a traditional network engineering role. You will own the full design problem space: reasoning through topology selection, rack layout implications, power and thermal constraints, cable plant feasibility, and fabric scaling — all the way from requirements intake through design documentation that deployment teams execute against. Each customer engagement may involve a different GPU platform, a different network topology, and a different set of physical constraints. You must be able to reason from first principles through novel design challenges rather than pattern-match to a single reference architecture.

You will work closely with cross-functional partners in Hardware, DC Operations, ICT/Structured Cabling, Software Engineering, and Validation to ensure your designs are not just technically sound but physically buildable and operationally sustainable. Success means producing network designs that deployment teams can execute without ambiguity, that scale to the customer’s target, and that meet performance requirements on the first turn-up.

Focus

• End-to-End Network Design: Own the design lifecycle from customer requirements through deployable architecture. Produce topology designs, IP/addressing schemes, routing policy, and fabric configuration specifications for AI training and inference fabrics. Design front-end (out-of-band management, customer access), back-end (GPU-to-GPU training fabric), and storage network architectures.

• Multi-Customer Architecture Adaptability: Design network architectures that adapt to different GPU platforms (NVIDIA, AMD, custom accelerators), server form factors, and workload profiles. Each customer engagement may require a different rack layout, power envelope, cable infrastructure approach, and fabric topology.

• Physical Infrastructure Integration: Translate logical network designs into physical reality. Work cross functionally on rack elevation planning, power distribution constraints, structured cabling architecture (fiber trunk design, patch panel layouts, cable pathway routing), and cooling/airflow considerations that impact network equipment placement. Ensure designs are buildable within the physical constraints of each facility.

• Design Documentation & Handover: Produce comprehensive design packages that enable deployment teams to execute independently. This includes High-Level Designs (HLDs), Low-Level Designs (LLDs), cutsheet specifications, bill of materials, cabling matrices, and design decision records. Your documentation is the contract between design intent and deployment execution.

• RDMA & High-Performance Fabric Design: Design lossless Ethernet fabrics optimized for RDMA (RoCEv2) workloads including PFC configuration, ECN tuning, traffic class design, and congestion management. Understand the relationship between fabric topology, ECMP behavior, and collective communication patterns in distributed training workloads.

• Cross-Functional Design Collaboration: Partner with Hardware Engineering on server/GPU platform integration, DC Operations on facility constraints and power planning, ICT on structured cabling feasibility and fiber budgets, Software Engineering on automation requirements and DCIM data modeling, and Validation teams on test plans and acceptance criteria. Your designs must satisfy constraints across