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# Storage Engineer (AI Infrastructure) - Hosting

- **Company:** Hamilton Barnes
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Bash Shell, Big Data, Computer Clusters, Data Infrastructure, File Systems, Distributed Data Store, InfiniBand, Storage Area Network (SAN), Python (Programming Language), Linux System Administration, Network File Systems, Performance Tuning, Remote Direct Memory Access, Ansible, Data Streaming, AI Infrastructure, Data Storage Technologies, High Performance Computing, Storage Technologies, Low Latency, Data Management, Machine Learning Operations, Data Pipelines, Nvme
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_d371d1ab21d8fc91908e40dd3ec41ac4

## About the Role

* Deep experience in storage engineering within HPC, AI infrastructure, hyperscale, or large-scale data center environments
 * Deep hands-on expertise with VAST Data storage platforms strongly preferred
 * Strong understanding of high-performance distributed storage architectures and parallel file systems
 * Experience supporting GPU-intensive AI/ML workloads and high-throughput data environments
 * Strong Linux systems administration skills
 * Experience troubleshooting performance across storage, networking, and compute layers
 * Familiarity with NFS, RDMA, NVMe-oF, InfiniBand, and modern storage networking concepts
 * Automation and scripting experience using Python, Bash, or Ansible preferred
 * Strong understanding of scalability, resiliency, and data protection in enterprise storage environments

## Description

This is a great opportunity for a Senior Storage Engineer to take ownership of the high-performance storage layer underpinning large GPU clusters and AI workloads at scale. The ideal candidate will work closely with infrastructure, networking, and platform engineering teams to design and optimize storage environments capable of supporting massive throughput, low latency, and highly parallelized AI training workloads., * Design, deploy, and operate large-scale high-performance storage platforms supporting AI and HPC workloads

 * Manage and optimize distributed storage environments for large GPU training and inference clusters
 * Work closely with platform, compute, and networking teams to ensure end-to-end infrastructure performance
 * Troubleshoot storage bottlenecks, latency issues, throughput constraints, and data flow inefficiencies
 * Contribute to storage architecture strategy, scalability planning, and operational best practices
 * Automate storage provisioning, monitoring, and lifecycle management processes
 * Support performance tuning across parallel file systems, object storage, and AI data pipelines
 * Implement observability and capacity planning solutions for petabyte-scale environments