AI Storage Solutions Expert

🕒 August 13

🏄 California, Texas – Remote

🏄 California – Remote 🤠 Texas – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Artificial Intelligence

👻 Ghost score 44%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.Why this score:

• Open for 91 days.
• Jobs at this company usually fill in 27 days.
• No salary listed.

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📋 Description

• Deploy and operate parallel/distributed storage systems including WEKA, VAST Data, Ceph, and DDN/Lustre • Design storage architectures optimized for AI checkpoint I/O bursts, sequential dataset reads, and inference KV cache • Implement multi-tenant storage isolation with per-tenant QoS, quotas, and access controls • Configure and optimize GPU Direct Storage for direct GPU-to-storage data paths • Deploy and manage storage networking, including NFS over RDMA, NVMe-oF, high-speed storage fabrics, and Nvidia CMX • Diagnose and tune storage performance using IOPS, throughput, latency profiling, fio, IOR, and mdtest • Own runbooks for common storage failure modes • Plan storage capacity for GPU cluster growth and customer workload projections • Manage firmware, data migration, and disaster-recovery procedures • Instrument storage telemetry and feed IO tail latency, checkpoint durations, NVMe SMART, filesystem health, and RDMA counters into the platform metrics/logs/traces store • Partner with the platform team to define the storage-fault predictor, including signals, incident-derived labels, and false-positive tolerances • Convert novel incidents into automation, progressing from SOPs to runbook-as-code and agent-executable remediation • Deliver observability and a baseline predictor for the top three storage-fault classes • Design storage systems for Nvidia GB200-class cluster deployments • Reduce storage-incident MTTR

🎯 Requirements

• 5+ years in enterprise or HPC storage operations • At least 2 years supporting AI/ML workloads • Hands-on deployment and operations experience with at least two of WEKA, VAST Data, Ceph, and DDN/Lustre • Strong understanding of AI training I/O patterns, including checkpoint frequency, dataset loading, and shuffle buffers • Experience with high-performance storage networking, including NFS over RDMA and NVMe-oF • Knowledge of GPU Direct Storage and RDMA-based data transfer • Proficiency in storage performance benchmarking and tuning with fio, IOR, and mdtest • Experience implementing multi-tenant storage with isolation and QoS • Strong Linux systems knowledge, including kernel tuning, filesystem internals, and block device management • Experience shipping an anomaly detector for storage/IO telemetry, or ability to articulate required labels and features • Runbook-as-code mindset; SOPs should be executable by a machine within a quarter

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