Storage Engineer (AI)

Location: San Francisco, United States

Salary: $150,000–$300,000 per year plus equity incentives

Company

hirify.global is building an open superintelligence stack for AI teams, combining compute, environments, evaluations, secure sandboxes, training, and deployment infrastructure.

What you will do

• Design and operate storage architectures for training datasets, checkpoints, 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 using 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 defined durability and availability targets.
• Diagnose performance and reliability issues across applications, clients, networks, filesystems, and storage devices while implementing access controls, quotas, monitoring, and runbooks.

Requirements

• 3+ years of experience building or operating production distributed storage systems.
• Hands-on experience with a 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 with Python, Go, Bash, or similar languages.
• Understanding of storage failure modes, data integrity, consistency, replication, recovery, networking, observability, and secure data lifecycle management.
• Experience with block, file, and object storage semantics, NVMe/SSD performance, filesystem tuning, I/O profiling, benchmarking, and capacity forecasting.

Nice to have

• Experience supporting large GPU training clusters and high-volume checkpoint workloads.
• Experience with S3-compatible object storage, data tiering, distributed caching, RDMA-enabled storage, or GPUDirect Storage.
• Experience with Kubernetes storage integrations or SLURM environments.
• Storage cost optimization experience or contributions to open-source storage systems.

Culture & Benefits

• Direct collaboration with customers training foundation models and deploying large-scale inference infrastructure.
• Work with engineering teams building infrastructure for frontier AI research and production systems.
• Direct impact on high-performance GPU infrastructure and large-scale AI workloads.
• Cash compensation of $150,000–$300,000 per year plus equity incentives.