# Staff Engineer, Distributed Storage and HPC & AI Infrastructure at Together AI

> Architect and scale multi-petabyte storage systems and Kubernetes storage operators for GPU training and inference workloads.

- Company: Together AI (together.ai)
- Location: Amsterdam
- Places: Amsterdam, Netherlands
- Posted: 2026-09-11
- Level: Staff
- Team: Distributed Storage and HPC & AI Infrastructure
- Work mode: Hybrid
- Apply: https://job-boards.greenhouse.io/togetherai/jobs/5226095007
- Page: https://ye.saranatour.dev/jobs/staff-engineer-distributed-storage-and-hpc-ai-infrastructure-together-ai-kn75ztf4aapd4wzc0kyagtykpd8hchks

## The posting

Staff Engineer, Distributed Storage and HPC & AI Infrastructure
Together AI · Amsterdam

### **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. 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, and tune parallel filesystems for AI workloads.

2 days a week in our office in Amsterdam

### **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.
- 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 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 projects and internal tooling.

### **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
- Distributed Storage Systems: Deep expertise in either of Ceph, WekaFS, Lustre, Vast, GPFS, or similar parallel filesystems at multi-petabyte scale
- Object Storage: 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 (TB/s aggregate cluster throughput - line saturation)
- Programming: Go and Python for automation, operators, and tooling
- Infrastr