Staff Storage Engineer – Frontier AI Infra

Prime-Intellect; San Francisco (CA); On-site; USD 150,000 - 300,000; Full time

Job summary

Prime Intellect is building the open superintelligence stack, delivering a full‑stack platform for frontier AI workloads. You will own and operate the storage systems feeding datasets, checkpoints, and model artifacts, balancing performance, durability, and cost as GPU clusters scale.

The role covers designing architectures, deploying parallel storage, and optimizing data paths for large-scale AI training and inference, with opportunities to influence end-to-end data lifecycles and security

Qualifications

• 3+ years building or operating production distributed storage systems.
• Hands-on experience with parallel or distributed filesystem or object storage platforms (Lustre/BeeGFS/Ceph/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, replication and recovery.

Responsibilities

• Design and operate storage architectures for training datasets, checkpointing and inference artifacts.
• Deploy and tune parallel filesystems, object storage, and NVMe caching for AI workloads.
• Benchmark throughput, latency, and metadata performance for training workloads.
• Build provisioning, capacity planning, lifecycle management, and automation for storage services.
• Test replication, recovery, backup, and durability targets.
• Diagnose performance and reliability issues across components.

Skills

Distributed storage

Linux administration

Scripting (Python/Go)

Performance tuning

Tools

Lustre

BeeGFS

Ceph

GPFS

Job description

Prime Intellect is building the open superintelligence stack, delivering a full‑stack platform for frontier AI workloads. You will own and operate the storage systems feeding datasets, checkpoints, and model artifacts, balancing performance, durability, and cost as GPU clusters scale.

The role covers designing architectures, deploying parallel storage, and optimizing data paths for large-scale AI training and inference, with opportunities to influence end-to-end data lifecycles and security