Staff Engineer (Distributed Storage, HPC & AI Infrastructure)

Join our team as a Staff Engineer specializing in Distributed Storage, HPC, and AI Infrastructure. In this role, you will design and deliver multi-petabyte storage systems for AI training and inference workloads. You will architect high-performance parallel filesystems and object stores, evaluate and integrate cutting-edge technologies, and drive cost optimization. Additionally, you will build Kubernetes-native storage operators and self-service platforms, optimize end-to-end data paths, and implement monitoring and alerting systems. This position offers competitive health insurance, retirement plans, and a flexible time off policy.

• Design and deliver multi-petabyte storage systems for AI workloads, architect high-performance parallel filesystems and object stores.
• Build Kubernetes-native storage operators and self-service platforms for automated provisioning and performance isolation.
• Optimize end-to-end data paths for high throughput, design multi-tier caching architectures, and implement intelligent prefetching.

• Storage benchmarking and profiling tools (fio, iperf3, iostat, blktrace)
• Linux Storage Stack: Advanced knowledge of filesystems (ext4, xfs), LVM, NVMe optimization, RAID configurations
• Kubernetes Storage: CSI drivers, StatefulSets, PersistentVolumes, storage operators, and custom controllers
• History of technical leadership: designing systems that significantly improved performance (>3x), reliability (99.9%+ uptime), or cost efficiency
• 8+ years in storage engineering with 3+ years managing distributed storage at multi-petabyte scale
• Storage optimization for GPU workloads, RDMA/InfiniBand networking, parallel filesystem optimization (100+ GB/s aggregate cluster throughput)
• Object Storage: Production experience with S3, MinIO, Ceph, or R2 including performance optimization and cost management
• Infrastructure as Code: Terraform, Ansible, Helm, GitOps (ArgoCD)
• 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
• Proven track record deploying and operating high-performance storage for GPU/HPC clusters
• Distributed Storage Systems: Deep expertise in WekaFS, Lustre, GPFS, BeeGFS, or similar parallel filesystems at multi-petabyte scale
• Programming: Go and Python for automation, operators, and tooling
• Deep Kubernetes and cloud-native storage experience in production environments
• Observability: Prometheus, Grafana, Thanos architecture and operations
• 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