Senior Storage Engineer (AI)

Location: Remote in the United States or United Kingdom. Travel to sites in the US and Europe is required as needed; in-person company retreats take place twice a year.

Company

hirify.global builds and operates an AI inference platform and its own distributed compute infrastructure, including AI inference pods with large GPU fleets.

What you will do

• Own the end-to-end storage architecture for durable object storage, per-pod high-throughput tiers, node-local NVMe caches, replication, and placement across the US and Europe.
• Design and build model distribution, including chunking, deduplication, prefetching, pinning, and working-set warming for new pods.
• Profile and optimize the path from disk to GPU memory across the kernel, file system, NVMe-oF or RDMA transport, GPUDirect Storage, and inference engine loaders.
• Select, benchmark, validate, and operate parallel and distributed file systems, object storage, NVMe hardware, and management software.
• Own capacity planning, monitoring, alerting, drive health, failure prediction, rebuilds, backup, recovery, reliability targets, and storage on-call.
• Automate pod bring-up, benchmarks, runbooks, triage, and tooling; collaborate with inference, platform, hardware, vendor, and site teams on production-scale storage.

Requirements

• 8+ years in storage or systems engineering, including several years designing and running storage with company-owned hardware at scale.
• Demonstrated experience building storage systems from design through petabyte-scale production.
• Deep hands-on Linux storage expertise covering the block layer, io_uring, NVMe queues, XFS, ext4, ZFS, page cache, and I/O profiling tools.
• Production experience with at least one parallel or distributed file system such as Lustre, WEKA, VAST, GPFS, or Ceph, plus S3-compatible object storage at petabyte scale.
• Strong knowledge of NVMe, NVMe-oF, RDMA transports, and storage networking, including PCIe, drives, and switches.
• Fluent in Go, Rust, or C/C++ and Python, with the ability to write tooling, benchmarks, and automation independently.
• Daily use of AI for scripting, benchmarking, analysis, documentation, and debugging, with the ability to apply LLM-based tools to small-team operations.
• Comfort owning latency, durability, and cost outcomes in a fast-moving, ambiguous environment.
• English fluency and the ability to travel to US and European sites as needed.

Nice to have

• Experience supporting large GPU fleets for AI training or inference, including GPUDirect Storage, NCCL-aware loading, model-weight caching, vLLM, SGLang, or TensorRT-LLM.
• Experience with InfiniBand, RoCE, and 400G+ Ethernet from the storage side.
• Experience with content-addressed or deduplicated storage, erasure coding, and multi-region replication and consistency.
• Kernel or driver contributions, or familiarity with SPDK, DPDK, or user-space storage stacks.
• Experience with Kubernetes CSI, Ceph, Rook on bare metal, Ansible, Terraform, Prometheus, and Grafana.
• Familiarity with storage hardware qualification, firmware, enclosures, backplanes, spares, and RMA economics.
• Experience meeting data residency, sovereignty, or compliance requirements across jurisdictions.

Culture & Benefits

• Remote-first work with core hours for collaboration and calls and flexible scheduling outside core hours.
• In-person company retreats twice a year.
• Meaningful stock options.
• Generous paid time off, including vacation, sick days, and public holidays.
• Paid maternity, paternity, and caregiver leave, with downtime following intense release cycles.