Storage Engineer (AI Infrastructure)

Location: San Francisco or Remote

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

Company

hirify.global is building an open superintelligence stack that provides frontier AI infrastructure for research teams, enterprises, and AI companies.

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 devices while collaborating with compute and networking teams.

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, and recovery.
• Knowledge of storage semantics, NVMe/SSD performance, filesystem tuning, I/O profiling, storage networking, observability, access control, encryption, and secure data lifecycle management.

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

• Work directly with customers training foundation models and deploying large-scale inference infrastructure.
• Collaborate with an engineering team building open frontier AI infrastructure.
• Have direct impact on reliable, high-performance GPU infrastructure.
• Receive cash compensation of $150,000–$300,000 plus equity incentives.