HPC Storage Engineer - Production Engineer, AI Storage HPC Clusters

We have an exciting opportunity for Production Storage Engineers – AI/HPC Infrastructure.

We are looking for experienced engineers with strong expertise in distributed storage systems, HPC storage, Linux, and high-performance file systems. Candidates should have hands-on experience building, managing, and optimizing large-scale production storage infrastructure.

The customer's Cloud Foundation AI Services team builds and operates the storage that

let's AI researchers train models reliably and efficiently across cloud environments. We

connect high-performance file systems, object storage, and data-management services to

AI compute, so training datasets and model checkpoints are fast and available wherever

they are needed.

We are looking for a hands-on storage system engineer who can turn demanding AI

workloads into reliable, scalable infrastructure. The work spans software development,

performance analysis, automation, and production ownership.

• Build and operate distributed file and object storage for AI training: shared researcher file systems, training datasets, and model checkpoints.
• Diagnose and improve end-to-end storage performance (metadata, throughput, latency, caching, network) so storage I/O stops stalling training.
• Automate the storage lifecycle (provisioning, scaling, upgrades, recovery) using Kubernetes and infrastructure-as-code.
• Move and migrate data across clusters, regions, and clouds without losing integrity or slowing researchers down.
• Keep it reliable through observability, alerting, testing, safe rollouts, and incident response, partnering with research, security, and storage-provider teams.

• Built or operated production distributed storage, high-performance file systems, or large-
• Strong Linux skills, and can debug file system, network, and concurrency issues in production.
• Writes maintainable software or automation in a language such as Python, Go, C++, or Rust.
• Public Has owned a reliability or performance improvement end to end, from spotting the problem through deployment and verification.

• Experience with cloud infrastructure and Kubernetes, or a comparable large-scale
• Depth in NFS/POSIX and distributed-file system internals: metadata, caching, consistency, availability, and recovery.
• Hands-on with one or more distributed or network storage systems such as Hammerspace, Lustre, ZFS/OpenZFS, VAST, or Pure Storage. Depth in comparable systems counts; you do not need every product named here.
• AI or HPC storage patterns: high-throughput data loading, checkpoint writes and restores, metadata-heavy access, and staging data for accelerator clusters.
• Infrastructure-as-code and observability tooling (Terraform, Helm, Kubernetes operators; metrics, tracing, benchmarking), and object/S3 storage with tiering and cross-region movement.

• Where strong candidates come from: HPC storage, cloud file system services, storage vendors, or storage-focused SRE/production-engineering teams. Prior model development experience is not required and should not be a filter.
• Sourcing /OpenZFS,