Staff Engineer, Distributed Storage and HPC & AI Infrastructure

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

• Architect and implement the technical strategy and storage roadmap for Together AI, driving high-performance architectural decisions as we scale our GPU fleet.
• Engineer and scale multi-petabyte AI/ML storage systems by integrating Vast, Weka, and Ceph while executing deep cost optimization through automated tiering and lifecycle policies.
• Develop intelligent caching and tiered storage architectures to achieve extreme IOPS and cluster-wide throughput at GPU scale for training and inference workloads, including GPU-direct (RDMA) delivery of model weights for fast cold starts and autoscaling.
• Design and build KV-cache offload and reuse for inference: tiering KV blocks across GPU HBM, host memory, local NVMe, and distributed cache, enabling prefix reuse across requests and nodes, and integrating with inference engines (vLLM, SGLang, TensorRT-LLM) to cut time-to-first-token.
• Tune storage isolation at the L2/L3 network layers to ensure secure, production-grade multi-tenancy for storage clients.
• Code Kubernetes storage operators and controllers to enable automated provisioning, self-service abstractions, and quota enforcement.
• Engineer end-to-end data paths to achieve 10+ GB/s per GPU node; architect multi-tier caching for model weights, KV cache, and datasets; tune parallel filesystems using advanced profiling; and scale storage infrastructure across thousands of nodes.
• Optimize end-to-end data paths through advanced benchmarking and profiling, contributing high-impact code to open-source storage and inference projects and internal tooling.

Requirements

• 8+ years in storage engineering, managing distributed storage at multi-petabyte scale
• Proven track record building, deploying and operating high-performance storage for GPU/HPC clusters, for training and/or inference
• Experience scaling storage for inference: KV-cache offload or disaggregated KV storage, prefix caching, model-weight distribution, or similar latency-critical data paths
• 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…