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Member of Technical Staff - Storage Infrastructure

Prime Intellect· Engineering
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Remote$150k - $300kFull-timePosted 5d ago

About the role

role focused on building and operating storage systems to support frontier AI workloads, ensuring high-throughput, durable access to datasets, checkpoints, and model artifacts as GPU clusters scale.

Responsibilities

  • Design and operate storage architectures for training datasets, checkpointing, inference artifacts, and shared research workflows
  • Deploy and tune parallel filesystems, object storage, and local NVMe caching for demanding AI workloads
  • Benchmark throughput, latency, metadata performance, and concurrent access with representative training and checkpoint workloads
  • Build provisioning, capacity planning, lifecycle management, and operational automation for storage services
  • Design and test replication, recovery, backup, and failure-handling procedures with explicit durability and availability targets
  • Diagnose performance and reliability issues across applications, clients, networks, filesystems, and devices
  • Implement access controls, tenant separation, quotas, monitoring, and runbooks; collaborate with compute and networking teams

Qualifications

  • 3+ years building or operating production distributed storage systems
  • Hands-on experience with at least one parallel or distributed filesystem or object storage platform, such as Lustre, BeeGFS, Ceph, or GPFS
  • Strong Linux administration and performance troubleshooting skills
  • Experience automating infrastructure operations in Python, Go, Bash, or similar languages
  • Understanding of storage failure modes, data integrity, consistency, replication, and recovery

Skills

Required:Storage architectures for AI workloadsParallel/filesystem/object storage deploymentPerformance benchmarkingAutomationReplication/backup/recovery design
Last updated Oct 21, 2026. Interested? Apply on the company site.

About Prime Intellect

Commoditizing Compute & Intelligence

Prime Intellect democratizes AI development at scale. Our platform makes it easy to find global compute resources and train state-of-the-art models through distributed training across clusters. Collectively own the resulting open AI innovations, from language models to scientific breakthroughs.

AI developmentbessemer-ai-infrastructure-2026compute resourcesdistributed trainingmodelsreinforcement-learning-platformsscience
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