Senior AI Storage Architect | Remote-First

Runware; Palo Alto (CA); Remote; USD 170,000 - 250,000; Full time

Benefits offered by this job

Stock options

Remote-first culture

Generous paid time off

Job summary

Runware is building the AI Factory with a distributed storage fabric across the US and Europe. We seek a Senior Storage Engineer to own the storage architecture for models, caches and customer data, and to design the data path from object storage to NVMe and GPU memory.

You will implement the first version and set the storage standard across pods. You will work hands-on with inference, platform and hardware teams, optimize load latency, and automate benchmarks using AI-driven approaches to scale

Qualifications

• 8+ years designing and running storage at scale.
• Deep Linux storage expertise: block layer, NVMe, filesystems.
• Production experience with Lustre, Ceph or GPFS.
• Proficiency in Go, Rust or C/C++ and Python.
• Experience supporting GPU AI workloads is a plus.

Responsibilities

• Own end-to-end storage architecture for the fleet and models.
• Design and build the model distribution path across pods.
• Profile disk to GPU memory path and reduce latency.
• Benchmark, operate and tune storage systems across hardware.
• Set reliability, durability and data residency targets.
• Automate benchmarks, runbooks and tooling with AI.
• Collaborate with inference engine, platform and hardware teams.
• Ensure secure, compliant data handling in multi-region deployments.

Skills

Storage systems design

Linux systems engineering

Go / Rust / C/C++

Python scripting

AI-powered tooling

Travel readiness

Tools

Lustre

GPFS

Ceph

S3-compatible object storage

NVMe / NVMe-oF

Prometheus / Grafana

Job description

Runware is building the AI Factory with a distributed storage fabric across the US and Europe. We seek a Senior Storage Engineer to own the storage architecture for models, caches and customer data, and to design the data path from object storage to NVMe and GPU memory.

You will implement the first version and set the storage standard across pods. You will work hands-on with inference, platform and hardware teams, optimize load latency, and automate benchmarks using AI-driven approaches to scale