Senior Storage Engineer
New
R
RunwareAI infrastructure
United States. United Kingdom, We keep core hours for collaboration and calls.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years in storage or systems engineering
- Required Skills
- PythonGoRust
Requirements
- Have 8+ years of storage or systems engineering experience.
- Have designed and operated storage at a company that runs its own hardware at scale.
- Have built storage systems from design through petabyte-scale production.
- Bring current, hands-on Linux storage expertise, including the block layer, io_uring, NVMe queues, XFS, ext4, ZFS, and the page cache.
- Have production experience with at least one parallel or distributed file system, such as Lustre, WEKA, VAST, GPFS, or Ceph.
- Have experience with S3-compatible object storage at petabyte scale.
- Understand NVMe, NVMe-oF, RDMA transports, and storage networking.
- Be fluent in Go, Rust, or C/C++, and Python.
- Use AI tools for scripting, benchmarking, analysis, documentation, and debugging.
- Be able to travel to sites in the US and Europe as needed.
Responsibilities
- Own the fleet’s end-to-end storage architecture, including durable object storage, per-pod storage, node-local NVMe caches, replication, and placement.
- Design and build the model distribution path, including chunking, deduplication, prefetching, pinning, and warming new pods before production traffic.
- Profile the path from disk to GPU memory and remove model-load bottlenecks across the kernel, file system, NVMe-oF or RDMA transport, GPUDirect Storage, and inference-engine loader.
- Select, benchmark, and operate parallel and distributed file systems, object storage, NVMe drives, enclosures, and management software on production hardware.
- Set and meet reliability and durability targets; own capacity planning, monitoring, alerting, drive health, failure prediction, rebuilds, backup, recovery, and storage on-call.
- Create a documented, automated playbook for provisioning, validating, and warming storage for new pods.
- Partner with inference engine, platform, hardware, storage vendor, and site teams, and feed production learnings into future servers and pods.
- Automate benchmarks, runbooks, triage, and tooling with LLM-based agents.
- Own data residency and operational storage security, including encryption at rest, access control, retention, and in-country placement for sovereign deployments.
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