[Hiring] Senior Storage Engineer @Runware

Role Description

As Senior Storage Engineer, you will own the storage architecture of Runware's AI supercomputer: how model weights, caches and customer data move from object storage on two continents to the NVMe and GPU memory of the node that needs them. You will design the data path, build the first version with your own hands, and set the standard for how storage is run across every pod.

This is a hands-on senior individual-contributor role. You will:

• Specify the systems, benchmark them on real hardware, and resolve performance issues.
• Work directly with inference engine, platform and hardware teams, storage and NVMe vendors, and site teams deploying pods.
• Ensure cold models load onto a GPU anywhere in the fleet in seconds.

What you'll do

• Own the end-to-end storage architecture for the fleet, including:
• The durable object store that holds 400,000+ models.
• The per-pod high-throughput tier that feeds GPUs.
• The node-local NVMe cache.
• The replication and placement logic across the US and Europe.
• Design and build the model distribution path.
• Drive model load latency down and keep it down.
• Select, benchmark and operate the storage systems.
• Set and hit reliability and durability targets.
• Make storage a routine part of pod bring-up.
• Partner with inference engine, platform and hardware engineering.
• Use AI to accelerate the work.
• Own data residency and the operational side of storage security.

Qualifications

• 8+ years in storage or systems engineering.
• Track record of building storage systems.
• Deep, current, hands-on Linux storage expertise.
• Real experience with at least one parallel or distributed file system in production.
• Strong understanding of NVMe, NVMe-oF, RDMA transports.
• Fluent in a systems language (Go, Rust or C/C++) and Python.
• Use AI to accelerate your own output every day.
• Comfortable owning outcomes in a fast-moving, ambiguous environment.
• Able to travel to sites in the US and Europe as needed.

Nice to have

• Experience feeding large GPU fleets for AI training or inference.
• Hands-on experience with InfiniBand or RoCE fabrics.
• Experience with content-addressed or deduplicated storage.
• Kernel or driver contributions or familiarity with SPDK, DPDK.
• Experience with Kubernetes CSI, Ceph or Rook on bare metal.
• Familiarity with the storage hardware supply chain.
• Experience building or running storage to meet data residency, sovereignty or compliance requirements.

Benefits

• A rare mandate to design the storage layer of an AI supercomputer.
• Meaningful stock options.
• Remote-first collective with in-person meetings twice a year.
• Fast and intense release cycles followed by real downtime.
• Generous paid time off, including vacation, sick days and public holidays.
• Paid maternity, paternity and caregiver leave.
• Twice-yearly company retreats in inspiring locations.