About the RoleIn 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.
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)
CompensationWe offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $250,000 - $300,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.
