Senior AI Storage Architect - Remote-First & Stock Options

Runware; Oregon; Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Stock options

Remote-first culture

Generous paid time off

Parental leave

Company retreats

Job summary

Runware is building an AI inference platform and storage powerhouse. We seek a Senior Storage Engineer to own the storage architecture across a fleet of AI pods, moving weights, caches and customer data efficiently between continents.

You will design the data path, benchmark on real hardware, and set the standard for storage across all pods. You will work hands-on with the inference engine, platform and hardware teams, optimize latency from disk to GPU memory, and ensure secure, compliant data

Qualifications

• 8+ years in storage or systems engineering, designing and running storage at scale.
• Hands-on Linux storage expertise: block layer, NVMe, file systems (XFS/ext4/ZFS).
• Experience with parallel/distributed file systems (Lustre, GPFS, Ceph) and petabyte-scale object storage.

Responsibilities

• Own end-to-end storage architecture for the fleet: durable object store, per-pod high-throughput tier, and NVMe cache.
• Design and build the model distribution path and warm-up for new pods.
• Drive load latency down end-to-end from disk to GPU memory and optimize the data path.
• Set reliability and durability targets, capacity planning, monitoring, and on-call for storage.
• Automate storage bring-up for new pods and collaborate with engine, platform, and hardware teams.
• Use AI to accelerate benchmarks, tooling, and runbooks.
• Ensure data residency and security across deployments.

Skills

Storage engineering

Linux proficiency

NVMe/PCIe knowledge

Go/Python scripting

Networking (RDMA/NVMe-oF)

Education

Bachelor's degree in CS or related field

Tools

Ceph

GPFS

Lustre

S3-compatible storage

Prometheus/Grafana

Job description

Runware is building an AI inference platform and storage powerhouse. We seek a Senior Storage Engineer to own the storage architecture across a fleet of AI pods, moving weights, caches and customer data efficiently between continents.

You will design the data path, benchmark on real hardware, and set the standard for storage across all pods. You will work hands-on with the inference engine, platform and hardware teams, optimize latency from disk to GPU memory, and ensure secure, compliant data