# Senior Storage Engineer at Runware

Runware is hiring a Senior Storage Engineer for a remote role open to applicants in United States. Level rates it Works on AI ●●●○; you can [apply on Runware](https://jobsbylevel.com/go/acd84862-68a8-4abd-b42b-54ed18d7c404?utm_medium=ai-agent).

AI Level 3, AI centrality 65 out of 100. Remote (United States | United Kingdom).

## Details

- Company: [Runware](https://jobsbylevel.com/companies/runware)
- AI level: AI Level 3 (score 65 out of 100)
- Location: Remote (United States | United Kingdom)
- Posted: November 4, 2026
- Apply: https://jobsbylevel.com/go/acd84862-68a8-4abd-b42b-54ed18d7c404?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-storage-engineer-at-runware-8d14fe

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## Description

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 ). Every one of those 400,000 models has to be on the right GPU, in the right pod, in seconds. Storage is what makes that possible. That is the job. The role As Senior Storage Engineer, you will own the storage architecture of Runware's AI supercomputer: how model weights, caches and customer data move from object storage on two continents to the NVMe and GPU memory of the node that needs them. You will design the data path, build the first version with your own hands, and set the standard for how storage is run across every pod. This is a hands-on senior individual-contributor role. You will specify the systems, benchmark them on real hardware, find the reason a 70 GB checkpoint took four minutes to load instead of twenty seconds, and then fix it at the root so it never happens again. You will work directly with our inference engine, platform and hardware teams, with our storage and NVMe vendors, and with the site teams deploying pods. Success means a cold model loads onto a GPU anywhere in the fleet in seconds, a new pod has its working set warm before it takes traffic, and a disk, node or whole site failing is invisible to the developer calling our API. What you'll do Own the end-to-end storage architecture for the fleet: the durable object store that holds 400,000+ models, the per-pod high-throughput tier that feeds GPUs, the node-local NVMe cache, and the replication and placement logic that ties them together across the US and Europe. Design and build the model distribution path. Decide how weights get from the source of truth to a pod, how they are chunked, deduplicated, prefetched and pinned, and how a new pod warms its working set before it takes production traffic. Drive model load latency down and keep it down. Profile the full path from disk to GPU memory, including the kernel, file system, NVMe-oF or RDMA transport, GPUDirect Storage and the inference engine's loader, and remove the bottleneck wherever it sits. Select, benchmark and operate the storage systems themselves: parallel and distributed file systems, object storage, NVMe drives and enclosures, and the software that manages them. Validate every choice on our hardware before it ships to a pod. Set and hit reliability and durability targets. Own capacity planning, monitoring, alerting, drive health and failure prediction, rebuild behaviour, backup and recovery, and on-call for the storage layer. Make storage a routine part of pod bring-up: a documented, automated playbook so that the storage tier of the next 1 MW pod is provisioned, validated and warm in hours, not weeks. Partner with inference engine, platform and hardware engineering so that what we design is operable at scale, and feed what you learn from production back into the next generation of servers and pods. Use AI to accelerate the work: automate benchmarks, runbooks, triage and tooling with LLM-based agents, so that a very small team can run storage for a very large fleet. Own data residency and the operational side of storage security: encryption at rest, access control, retention and in-country placement for sovereign deployments. Requirements What we're looking for 8+ years in storage or systems engineering, with several of those designing and running storage

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