Staff Engineer, Distributed Storage,HPC & AI Infrastructure

Together Computer Inc; Amsterdam; Hybrid; EUR 120,000 - 180,000; Full time

Job summary

Together AI is seeking a highly experienced storage engineer to design and deliver multi-petabyte storage systems for AI training and inference workloads. You will architect high-performance parallel filesystems and object stores, integrating WekaFS, Ceph, Lustre, and driving cost optimization and scalability across large clusters.

Your role includes building Kubernetes-native storage operators, ensuring multi- tenancy, automated provisioning, and high uptime while collaborating with ML/SRE

Qualifications

• 8+ years in storage engineering 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: improving performance or uptime or cost efficiency.
• Distributed Storage Systems: expertise in WekaFS, Lustre, GPFS, BeeGFS at multi-petabyte scale.
• Object Storage: experience with S3/MinIO/Ceph or compatible, performance optimization and cost management

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 storage best practices; contribute to open-source; write docs, postmortems, and public learnings.

Skills

Storage engineering

Distributed storage

Kubernetes

Go

Python

Linux storage

Networking

Cost optimization

Leadership

Performance tuning

Education

BS/MS in Computer Science or related

Tools

WekaFS

Ceph

Lustre

GPFS

BeeGFS

NVMe-oF

iSCSI

RDMA

Terraform

Helm

Ansible

GitOps

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-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.

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)


• 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.

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 identity, veteran status, and more.

Voluntary Self-Identification

For government reporting purposes, we ask candidates to respond to the below self-identification survey.Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiringprocess or thereafter. Any information that you do provide will be recorded and maintained in aconfidential file.

As set forth in Together AI’s Equal Employment Opportunity policy,we do not discriminate on the basis of any protected group status under any applicable law.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection.As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measurethe effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categoriesis as follows:

A \"disabled veteran\" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service-connected disability.

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An \"Armed forces service medal veteran\" means a veteran who, while serving on active duty in the U.S. military, ground, naval or air service, participated in a United States military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.