Staff Engineer, Distributed Storage,HPC & AI Infrastructure
Together AI12 hours ago
Amsterdam, NetherlandsStaff+
Responsibilities
- Design and operate multi-petabyte AI/ML storage systems using parallel filesystems and object stores.
- Lead capacity planning, intelligent tiering, lifecycle policies, right-sizing, and storage cost optimization.
- Design and optimize RDMA, InfiniBand, 400GbE, NVMe-oF, iSCSI, and TCP/IP storage networking.
- Build Kubernetes storage operators, controllers, automated provisioning, self-service abstractions, multi-tenant isolation, and quota enforcement.
- Optimize caching, data locality, prefetching, model-weight distribution, parallel filesystems, and end-to-end data paths for GPU workloads.
- Implement monitoring, alerting, SLOs, disaster recovery, backups, chaos engineering, and automated remediation.
- Partner with ML and SRE teams, mentor engineers, contribute to open source, and write documentation and postmortems.
Requirements
- 8+ years of storage engineering experience, including 3+ years managing distributed storage at multi-petabyte scale.
- Proven experience deploying and operating high-performance storage for GPU/HPC clusters.
- Deep production experience with Kubernetes and cloud-native storage.
- Strong Go and Python programming skills for production-grade tools, automation, operators, and controllers.
- BS/MS in Computer Science, Engineering, or equivalent practical experience.
- Technical leadership experience delivering major improvements in performance, reliability, or cost efficiency.
- Deep expertise with WekaFS, Lustre, GPFS, BeeGFS, or similar parallel filesystems at multi-petabyte scale.
- Production experience with S3, MinIO, Ceph, or R2, including performance optimization and cost management.
- Experience with Kubernetes storage components including CSI drivers, StatefulSets, PersistentVolumes, storage operators, and custom controllers.
- Experience optimizing storage for GPU workloads, RDMA/InfiniBand networking, and high-throughput parallel filesystems.
- Experience with Terraform, Ansible, Helm, and GitOps tools such as ArgoCD.
- Advanced knowledge of ext4, XFS, LVM, NVMe optimization, and RAID configurations.
- Experience operating Prometheus, Grafana, and Thanos.
- Preferred experience with GPU Direct Storage, NVMe-oF, 100GbE/400GbE networking, ML/AI storage patterns, controller-runtime, Kubebuilder, snapshots, cloning, thin provisioning, Velero, Restic, cross-region replication, storage encryption, compliance, fio, iperf3, iostat, and blktrace.
Benefits
- Hybrid work arrangement with two days per week in the Amsterdam office.
- Opportunity to work on next-generation AI infrastructure at a research-driven AI company.
- Opportunities to contribute to open-source projects, research, documentation, and public technical learnings.
About Together AI
Together AI builds an AI-native cloud platform for developers, offering high-performance inference, fine-tuning/model shaping, and large-scale pre-training on on-demand GPU clusters with APIs and managed services. It emphasizes open-source models that teams can run and adapt, and also provides infrastructure for decentralized and scalable workloads. Founded in 2022 and headquartered in San Francisco, it is privately held and reports notable customers including Cursor, ElevenLabs, Salesforce, and Zoom.