AI infrastructure with on-demand GPUs and serverless compute. Run training, inference, and batch workloads on the cloud with Runpod. Backed by AI Grant.
About the role
Own capacity, durability, availability, and performance characteristics of network volumes, local NVMe, and S3-compatible object storage. Tune the full I/O path: device and filesystem configuration, caching and read-ahead strategies, replication and erasure coding trade-offs, and client-side mount behavior.
What they're looking for
- 8+ years in infrastructure, storage, or systems engineering, with substantial ownership of production storage at scale
- Deep, practical experience with at least one distributed storage system — Ceph, MinIO, Lustre, GPFS/Spectrum Scale, MooseFS, WekaFS, VAST, ZFS-based systems, or comparable
- Strong Linux internals and storage-stack knowledge: block layer, filesystems, NVMe, page cache, I/O schedulers, NFS/SMB, iSCSI/NVMe-oF
- Building and/or operating S3-compatible object storage services
- Solid networking fundamentals with specific experience tuning networks for storage workloads
- Proficiency in writing and shipping production code in Go, Python, Rust, or similar (not just scripting)
More about this role
Runpod is the AI Developer Cloud. More than one million developers, from indie researchers to teams running frontier models in production, use Runpod to experiment, train, fine-tune, deploy, and scale AI on one platform. The platform has processed more than 20 billion inference requests. We closed a $100M Series A in June 2026. We're at an inflection point for AI infrastructure, and we're building the platform the next generation of developers will depend on.
We're a small, remote-first team. We take ownership seriously, move fast, and ship work that more than a million developers rely on every day. We're looking for people who care deeply, build with urgency, and want to matter at scale.
Learn more in our CEO's funding announcement: https://www.runpod.io/blog/one-million-developers .
You will be part of the Infrastructure organization, specifically within the team managing Runpod's multi-region storage ecosystem. Including network volumes, local NVMe, and S3-compatible object storage. At Runpod, storage is a critical, high-impact resource; it determines cold start velocity, training job data streaming efficiency, and the reliable persistence of model weights and checkpoints.
This...
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