About Runware Runware is the AI inference platform that builds and runs its own infrastructure, end to end. Our Sonic Inference Engine has served more than 20 billion requests for over 1 million developers and 500 million end users, with 400,000+ models behind a single integration, at up to 10x lower cost per token than traditional providers. We are now building the AI Factory that runs it. The Sonic Inference Pod packs 1 MW of custom-designed compute into a 20-foot container with closed-loop, water-free cooling, sited wherever power exists. Pods are live in the US and Europe, and we are scaling to dozens of pods and tens of thousands of GPUs, all operated as one distributed fabric that reroutes traffic the moment a pod goes offline (announcement). The network is what turns those pods into a supercomputer. That is the job. The role As AI Factory Network Architect, you will own the network architecture of Runware's AI supercomputer, from the NVLink domain inside a server to the optical links between pods on two continents. You will set the design, build the first systems with your own hands, and grow the team that runs them. This is a hands-on leadership role, not a slide-deck role. You will specify the fabrics, validate them on real hardware, debug the first congestion collapse at 3 a.m., and then write the standard so it never happens again. You will work directly with our hardware, platform, and inference engineering teams, with NVIDIA and our optical and switching vendors, and with the site teams deploying pods. Success means inference traffic flows across tens of thousands of GPUs at line rate, pods join the fabric in days rather than weeks, and a failure anywhere in the network is invisible to the developer calling our API. What you'll do
- Own the end-to-end network architecture for the AI Factory: the scale-up fabric (NVLink and NVSwitch domains), the scale-out fabric (InfiniBand and RoCE), the storage and management networks, and the pod-to-pod and inter-site backbone.
- Design the optical layer: transceiver and cable selection, co-packaged and linear-drive optics evaluation, DWDM for pod-to-pod and metro links, and the link budgets and reliability targets behind them.
- Define the reference network design for the Sonic Inference Pod so every new pod ships with a validated, repeatable fabric and joins the global network in days.
- Build and bring up the first fabrics yourself: rail-optimized topologies, subnet manager and routing configuration, adaptive routing, congestion control, QoS, and GPUDirect RDMA tuning, measured against inference throughput and tail latency rather than synthetic benchmarks.
- Work with the inference engine team so the Sonic Inference Engine's traffic patterns, from tensor- and pipeline-parallel collectives to KV-cache transfer and multi-node serving, drive topology and buffer decisions, not the other way round.
- Run capacity planning and the vendor roadmap with NVIDIA, switch and optics suppliers, and carriers; negotiate technical specifications and lead evaluations of new fabric generations.
- Set the operating standard: telemetry, link-health monitoring, failure-domain design, automated fault isolation, and runbooks that let a small team operate tens of thousands of ports.
- Hire and lead a team of network engineers as the footprint grows, and act as the final technical escalation for fabric incidents across all sites.
Requirements What we're looking for
- 15+ years designing, building, and operating high-performance networks, with a significant part of that in HPC or AI supercomputers at the scale of thousands of GPUs or more.
- Deep, hands-on InfiniBand experience: fabric design, subnet management, routing engines, adaptive routing, congestion control, and performance debugging at the port and queue-pair level.
- Working knowledge of NVLink and NVSwitch domains and how scale-up and scale-out fabrics interact for multi-GPU and multi-node inference and training.
- Optical networking depth: transceivers and cabling at 400G and 800G, link budgets, DWDM and coherent optics for inter-site links, and the operational realities of optics at scale (failure rates, cleaning, inventory).
- Hands-on RDMA experience across InfiniBand and RoCE, including GPUDirect, NCCL and collective behaviour, PFC and ECN tuning, and the trade-offs between the two.
- A track record of leading network teams through greenfield builds and production incidents, and of being the person engineers call when the fabric is doing something nobody understands.
- Comfort automating everything: Python or Go, infrastructure as code, and telemetry pipelines rather than CLI sessions.
- Clear writing and the ability to turn a design into a document that a site team on another continent can execute from.
- Willingness to travel to pod sites in the US and Europe for bring-up and major changes.
Strong differentiators
- Experience with NVIDIA Spectrum-X, Quantum-X800, or Blackwell-generation NVLink systems, or the equivalent from a hyperscaler's in-house stack.
- Hands-on time with co-packaged optics, linear-drive pluggables, or photonic switching evaluations.
- Experience designing networks for modular or containerized data centers, edge sites, or deployments with constrained power and space.
- Background in inference-specific traffic patterns: disaggregated prefill and decode, KV-cache transfer, and multi-tenant serving across a shared fabric.
- Contributions to SONiC, OpenFabrics, UEC, or similar open networking efforts.
- Experience with sovereign or data-residency network designs, including inter-site encryption and segmentation.
- Prior work at a GPU cloud, hyperscaler, national lab, or AI lab building its own supercomputer.
Benefits Why Runware You will design the network for an AI supercomputer that is being built right now, with the budget, the hardware, and the mandate to do it properly. Few network architects get to own a fabric from the first pod to the global
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