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

Rezolve Ai; Stati Uniti; Remoto; USD 120.000 - 180.000; Tempo pieno

Descrizione del lavoro

Rezolve Ai is seeking a hands-on HPC Storage Engineer for production AI storage across cloud environments. You will build and operate high-performance filesystems and distributed storage for AI training datasets and model checkpoints.

The role emphasizes Linux proficiency, performance optimization, and automation using Python/Go/C++/Rust, with strong collaboration across research and security teams. This is a remote US-based opportunity with scalable infrastructure.

Competenze

• Experience building or operating production distributed storage systems.
• Hands-on Linux system administration and debugging of storage/network issues.
• Ability to write maintainable automation code in Python/Go/C++/Rust.

Mansioni

• Build and operate distributed file and object storage for AI training and datasets.
• Diagnose and optimize end-to-end storage performance (throughput, latency, metadata).
• Automate storage lifecycle (provisioning, scaling, upgrades, recovery) using Kubernetes and IaC.
• Move data across clusters/regions/clouds without integrity loss.
• Maintain reliability via observability, alerting, testing and incident response.

Conoscenze

Distributed storage

HPC storage

Linux

High-performance filesystems

Programming (Python/Go/C++/Rust)

Strumenti

Kubernetes

Terraform

Helm

Infrastructure as Code

Descrizione del lavoro

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

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-
• 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
• 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,

Thanks & Regards

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