Staff Engineer, Distributed Storage,HPC & AI Infrastructure

Togetherai; Amsterdam; Hybrid; EUR 150,000 - 210,000; Full time

Job summary

Together AI is seeking a seasoned Storage Systems Architect to design and deliver multi-petabyte AI storage architectures, integrating WekaFS, Ceph, Lustre, and GPFS. You will optimize end-to-end data paths, build Kubernetes-native storage operators, and enable automated provisioning with strict multi-tenancy and quotas at cluster scale.

You will lead capacity planning, cost optimization (30-50% savings via tiering and right-sizing), and collaborate with ML/SRE teams while mentoring peers and

Qualifications

• 8+ years in storage engineering with 3+ years distributing 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 tools
• BS/MS in Computer Science or Engineering or equivalent
• History of technical leadership: designs improving performance or cost efficiency
• Expertise in WekaFS, Lustre, GPFS, BeeGFS at scale
• Production experience with S3/MinIO/Ceph or similar object storage

Responsibilities

• Design multi-petabyte AI/ML storage systems and integrate WekaFS, Ceph, and others; lead capacity planning and cost optimization
• Design/optimize RDMA, InfiniBand, 400GbE networks; tune for max throughput and low latency
• Build Kubernetes storage operators/controllers for automated provisioning and multi-tenant isolation
• Deliver 10-50 GB/s per GPU node; optimize caching, parallel filesystems, and data paths
• Build multi-tier caches (local NVMe, distributed, object); optimize data locality and distribution
• Implement monitoring, alerting, SLOs; design DR/backups and run chaos engineering
• Partner with ML/SRE teams; mentor storage best practices; contribute to open source; write docs

Skills

Go

Python

Distributed storage

Leadership

Education

BS/MS in Computer Science or Engineering

Tools

WekaFS

Ceph

Lustre

GPFS

BeeGFS

CSI drivers

Terraform

Ansible

Helm

GitOps (ArgoCD)

Job description

About the Role

In this role, you will design and deliver multi-petabyte storage systems purpose-built for the world’s largest AI training and inference workloads. You’ll architect high-performance parallel filesystems and object stores, evaluate and integrate cutting-edge technologies such as WekaFS, Ceph, and Lustre, and drive aggressive cost optimization-routinely achieving 30-50% savings through intelligent tiering, lifecycle policies, capacity forecasting, and right-sizing. 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-end, 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.

Hybrid Working

2 days a week at our offices in Amsterdam

Responsibilities

• Design multi-petabyte AI/ML storage systems; integrate WekaFS, Ceph, etc.; lead capacity planning and cost optimization (30-50% savings via tiering, lifecycle policies, right-sizing).
• Design/optimize RDMA, InfiniBand, 400GbE networks; tune for max throughput/min latency; implement NVMe-oF/iSCSI; troubleshoot bottlenecks; optimize TCP/IP for storage.
• Build Kubernetes storage operators/controllers; enable automated provisioning, self-service abstractions, multi-tenant isolation, quotas; create reusable Helm/Terraform patterns.
• Deliver 10-50 GB/s per GPU node; optimize caching (weights/datasets/checkpoints), parallel filesystems, and data paths; troubleshoot with profiling tools; scale to thousands of nodes.
• Build multi-tier caches (local NVMe, distributed, object); optimize data locality and model-weight distribution; implement smart prefetching/eviction.
• Implement monitoring, alerting, SLOs; design DR/backups with runbooks; run chaos engineering; ensure 99.9%+ uptime via proactive/automated remediation.
• Partner with ML/SRE teams; mentor on storage best practices; contribute to open-source; write docs, postmortems, and public learnings.

Requirements

• 8+ years in storage engineering with 3+ years 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 tools.
• BS/MS in Computer Science, Engineering, or equivalent practical experience.
• History of technical leadership: designing systems that significantly improved performance (>3x), reliability (99.9%+ uptime), or cost efficiency.
• Distributed Storage Systems: Deep expertise in WekaFS, Lustre, GPFS, BeeGFS, 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 (100+ GB/s aggregate cluster throughput).
• Programming: Go and Python for automation, operators, and tooling.
• Infrastructure as Code: Terraform, Ansible, Helm, GitOps (ArgoCD).
• Linux Storage Stack: Advanced knowledge of filesystems (ext4, xfs), LVM, NVMe optimization, RAID configurations.
• Observability: Prometheus, Grafana, Thanos architecture and operations.

Nice to Have Skills

• GPU Direct Storage (GDS), NVMe-oF, storage networking (100GbE/400GbE).
• ML/AI storage patterns (model weights, checkpointing, dataset caching).
• Kubernetes operator development (controller-runtime, kubebuilder).
• Storage snapshots, cloning, and thin provisioning.
• Backup and disaster recovery (Velero, Restic, cross-region replication).
• Storage encryption (at-rest and in-transit), security and compliance.
• Storage benchmarking and profiling tools (fio, iperf3, iostat, blktrace).

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Equal Opportunity

Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender…