Staff Engineer, Distributed Storage and HPC & AI Infrastructure

Highlights

• Operate multi-petabyte storage built for massive AI training and inference.
• Engineer end-to-end data paths hitting 10+ GB/s per GPU node at scale.
• Drive storage roadmap while scaling GPU fleet for frontier AI workloads.

What they offer

• Startup equity
• Health insurance
• Other benefits
• Flexibility in terms of remote work

Ideal candidate

Seasoned storage leader who thrives at extreme scale and loves squeezing maximum throughput for GPU workloads. You care about reliability, cost efficiency, and turning complex infrastructure into self-service platforms.

Requirements

• 8+ years in storage engineering, managing distributed storage at multi-petabyte scale
• Proven track record deploying and operating high-performance storage for GPU/HPC clusters
• Deep Kubernetes and cloud-native storage experience in production environments
• Strong coding skills in Go and Python with demonstrated ability to build production-grade systems and tooling
• BS/MS in Computer Science, Engineering, or equivalent practical experience
• History of technical leadership: designing systems that significantly improved performance, reliability (99.999%+ uptime), or cost efficiency
• Deep expertise in Ceph, WekaFS, Lustre, Vast, GPFS, or similar parallel filesystems at multi-petabyte scale
• Production experience with S3, MinIO, Ceph, or R2 including performance optimization and cost management
• Kubernetes Storage: CSI drivers, StatefulSets, PersistentVolumes, storage operators, and custom controllers
• Storage optimization for GPU workloads, RDMA/InfiniBand networking, parallel filesystem optimization
• Infrastructure as Code: Terraform, Ansible, Helm, GitOps (ArgoCD)
• Linux Storage Stack: filesystems (ext4, xfs), LVM, NVMe optimization, RAID configurations
• Observability: Prometheus, Grafana, Thanos architecture and operations