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Staff Engineer, Distributed Storage,HPC & AI Infrastructure

Together AI· Engineering
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Hybrid (Amsterdam, Netherlands)Full-timePosted 8mo ago

About the role

Design and deliver multi-petabyte storage systems for AI training/inference workloads, architect high-performance parallel filesystems and object stores, and build Kubernetes-native storage operators with multi-tenancy, QoS, and automated provisioning. Optimize data paths, implement multi-tier caching, and drive cost savings while ensuring high availability. Hybrid work: 2 days per week 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.

Qualifications

  • 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

Skills

Required:Storage design and optimizationKubernetes and cloud-native storageGoPython

Benefits

  • Not listed in the description
Last updated Oct 21, 2026. Interested? Apply on the company site.

About Together AI

The AI Acceleration Cloud - Fast Inference, Fine-Tuning & Training

Run and fine-tune generative AI models with simple APIs and scalable GPU clusters. Train & deploy at scale on The AI Acceleration Cloud.

AI native cloudFine-TuningGPU clustersInferenceTrainingai-infrastructure-and-developmentbrex-fastest-growing-2025liquid-ai-recruiting-target
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