Staff Engineer, Distributed Storage and HPC & AI Infrastructure

San Francisco, CA · full time · Lead / Staff / Principal

$250,000 – $300,000

Listed on Together AI’s own careers site. You apply with them directly — we never stand between you and the employer.

What this role is

Together AI is hiring a staff-level engineer to architect and operate massive-scale distributed storage systems purpose-built for AI training and GPU clusters, managing petabyte-scale infrastructure while optimizing data throughput and building Kubernetes-native storage platforms. This role suits deeply experienced storage engineers comfortable with complex distributed systems at extreme scale and seeking technical leadership opportunities in the AI infrastructure space.

Our summary, not Together AI’s wording. The full posting is on their site.

Skills this role names

What they ask for

Required

8+ years storage engineering experience at multi-petabyte scale
Proven deployment and operation of high-performance storage for GPU/HPC clusters
Production Kubernetes and cloud-native storage experience
Strong coding in Go and Python
BS/MS in Computer Science, Engineering, or equivalent practical experience
History of technical leadership with measurable improvements to performance, reliability, or cost efficiency
Deep expertise in at least one parallel filesystem: Ceph, WekaFS, Lustre, Vast, or GPFS
Production object storage experience with S3, MinIO, Ceph, or R2
Kubernetes storage knowledge including CSI drivers, StatefulSets, PersistentVolumes, and custom controllers
Storage optimization for GPU workloads and RDMA/InfiniBand networking
Advanced Linux storage stack knowledge including filesystems, LVM, NVMe, and RAID
Observability tools experience with Prometheus, Grafana, and Thanos

Nice to have

GPU Direct Storage (GDS)
NVMe-oF and storage networking
RDMA implementations
ML/AI storage patterns including model weights, checkpointing, and dataset caching
Storage benchmarking and profiling tools