AI Storage Solutions Expert

Bitdeer Technologies Group • San Jose, CA

13d • Onsite

About The Position

Bitdeer is building an AI-operated GPU cloud. Storage is where AI workloads either fly or fall over: a slow parallel read can starve a 1,000-GPU job; a stalled checkpoint can waste a full training epoch. In this role you deploy and operate the high-performance storage layer for AI training and inference across NeoCloud's US DCs, and you feed the AIOps substrate with the signals it needs to catch storage regressions before they page a customer.

Requirements

• 5+ years in enterprise or HPC storage operations, with at least 2 years supporting AI/ML workloads
• Hands-on deployment and operations experience with at least two of: WEKA, VAST Data, Ceph, DDN/Lustre
• Strong understanding of AI training I/O patterns: checkpoint frequency, dataset loading, shuffle buffers
• Experience with high-performance storage networking (NFS over RDMA, NVMe-oF)
• Knowledge of GPU Direct Storage and RDMA-based data transfer
• Proficiency in storage performance benchmarking and tuning (fio, IOR, mdtest)
• Experience implementing multi-tenant storage with isolation and QoS
• Strong Linux systems knowledge (kernel tuning, filesystem internals, block device management)
• Instinct for turning ops toil into ML signal — you've either shipped an anomaly detector for storage/IO telemetry or you can articulate the labels and features you'd need to.
• Runbook-as-code mindset — every SOP you write should be executable by a machine within a quarter.

Responsibilities

• Deploy and operate parallel/distributed storage systems: WEKA, VAST Data, Ceph, DDN/Lustre.
• Design storage architectures optimized for AI workload patterns — checkpoint I/O bursts, sequential dataset reads, KV cache for inference.
• Implement multi-tenant storage 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 (NFS over RDMA, NVMe-oF, high-speed storage fabrics) and Nvidia CMX for cluster-wide storage orchestration.
• Diagnose and tune storage performance: IOPS, throughput, latency profiling with fio, IOR, mdtest; own the runbook for common failure modes.
• Plan storage capacity aligned with GPU cluster growth and customer workload projections; manage firmware, data migration, and DR procedures.
• Instrument storage telemetry — IO tail latency, checkpoint durations, NVMe SMART, filesystem health, RDMA counters — into the metrics/logs/traces store the platform team runs.
• Partner with the platform team to define the storage-fault predictor: which signals, which labels (from your incidents), which false-positive tolerances.
• Convert every novel incident into an automation: SOPs become runbook-as-code, runbook-as-code becomes an agent-executable remediation.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed