Staff Engineer, Together AI

• 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
• Storage benchmarking and profiling tools (fio, iperf3, iostat, blktrace)

Desirable

• 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
• 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