HPC Storage Engineer - Production Engineer, AI Storage for 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 filesystems. Candidates should have hands-on experience building, managing, and optimizing large-scale production storage infrastructure.

Role: HPC Storage Engineer - Production Engineer, AI Storage for HPC Clusters

Location: Remote

Position Type: Fulltime

About the role

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 filesystems, 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.

What you'll do

· Build and operate distributed file and object storage for AI training: shared researcher

filesystems, 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.

Must-haves

· Built or operated production distributed storage, high-performance filesystems, or large-

scale data infrastructure.

· Strong Linux skills, and can debug filesystem, 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.

Nice to have

· Experience with cloud infrastructure and Kubernetes, or a comparable large-scale

orchestration environment.

· Depth in NFS/POSIX and distributed-filesystem 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.

Note:

· Where strong candidates come from: HPC storage, cloud filesystem services, storage

vendors, or storage-focused SRE/production-engineering teams. Prior model development

experience is not required and should not be a filter.

· Sourcing keywords: distributed filesystems, NFS, POSIX, Lustre, ZFS/OpenZFS,

Hammerspace, VAST, HPC storage, AI training I/O, checkpointing, S3, Linux performance,

Kubernetes operators, Terraform, Helm.

Thanks & Regards

Sumit Talekar

Associate Manager – TA

Rezolve AI Limited

Add: 499 Park Ave, Suite 1003, New York, NY 10022, USA

Contact: +1(646) 992-9557 | [email protected]

WebSite: rezolve.com