Staff Engineer, Distributed Storage & AI Infrastructure

Together AI·San Francisco·Posted 4 June 2026

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About the role

About the Role In this role, you will operate, scale, and optimize multi-petabyte storage systems purpose-built for the world’s largest AI training and inference workloads. Storage now sits on the inference critical path as well, so you’ll also build the caching and data-movement layers that deliver KV cache and model weights directly into GPU memory at fabric speed. You’ll manage and scale high-performance parallel filesystems and object stores, evaluate and integrate cutting-edge technologies such as Vast, Weka, Ceph, and Lustre, and solve the complex engineering challenges of operating at extreme throughput, low-latency data paths, and massive cluster-scale storage operations. You will also build Kubernetes-native storage operators and self-service platforms that provide automated provisioning, strict multi-tenancy, performance isolation, and quota enforcement at cluster scale. Day-to-day, you’ll optimize end-to-end data paths for 10-50 GB/s per node, design multi-tier caching architectures, implement intelligent prefetching and model-weight distribution, build KV-cache offload and reuse tiers for inference, and tune parallel filesystems for AI workloads. Responsibilities…

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