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 delivering 10+ GB/s per GPU node at scale.
• Drive storage roadmap and architecture as Together AI scales its GPU fleet.

Ideal candidate

A veteran storage engineer who thrives operating at massive scale and loves squeezing extreme throughput from complex systems. You lead technically, obsess over performance and reliability for GPU workloads, and build automated 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 for production-grade systems and tooling
• BS/MS in Computer Science, Engineering, or equivalent practical experience
• History of technical leadership improving performance, reliability or cost efficiency
• Deep expertise in Ceph, WekaFS, Lustre, Vast, GPFS or similar parallel filesystems
• Production experience with S3, MinIO, Ceph or R2 including optimization and cost management
• Kubernetes storage: CSI drivers, StatefulSets, PersistentVolumes, operators and controllers
• Storage optimization for GPU workloads, RDMA/InfiniBand and parallel filesystem tuning
• Infrastructure as Code: Terraform, Ansible, Helm, GitOps (ArgoCD)
• Advanced Linux storage stack: ext4, xfs, LVM, NVMe optimization, RAID
• Observability: Prometheus, Grafana, Thanos architecture and operations