Senior HPC Storage Engineer

Responsibilities

• Own capacity, durability, availability, and performance for network volumes, local NVMe, and S3-compatible object storage.
• Tune the full I/O path, including device and filesystem configuration, caching, read-ahead, replication, erasure coding, and client mount behavior.
• Diagnose production performance issues end to end.
• Lead capacity expansions, hardware refreshes, migrations, and rebalances without customer-visible disruption.
• Design and tune storage network paths, including high-throughput east-west fabrics, MTU and jumbo frames, congestion and flow control, multipath, and NIC/offload configuration.
• Optimize RDMA/RoCE and high-speed InfiniBand/Ethernet fabrics for storage traffic.
• Write production code in Go, Python, Rust, or similar for control-plane services, provisioning, data movement, and monitoring.
• Build and extend control-plane, S3-compatible, CSI, Kubernetes, vendor, and cloud-provider APIs.
• Automate operational workflows and manage infrastructure as code through code review, testing, and CI.
• Instrument storage fleets and build metrics, dashboards, SLOs, and alerts for IOPS, throughput, latency, errors, retries, capacity, and tenant consumption.
• Participate in storage-system on-call rotations and lead blameless post-incident follow-up.

Requirements

• 8+ years of experience in infrastructure, storage, or systems engineering, with substantial ownership of production storage at scale.
• Deep practical experience with at least one distributed storage system, such as Ceph, MinIO, Lustre, GPFS/Spectrum Scale, MooseFS, WekaFS, VAST, or ZFS-based systems.
• Strong Linux internals and storage-stack knowledge, including block devices, filesystems, NVMe, page cache, I/O schedulers, NFS/SMB, and iSCSI/NVMe-oF.
• Experience building or operating S3-compatible object storage services.
• Strong networking fundamentals and experience tuning networks for storage workloads.
• Proficiency shipping production code in Go, Python, Rust, or similar languages.
• Hands-on observability experience with Prometheus, Grafana, Datadog, or equivalent, including metric design.
• Track record of performance analysis and debugging under production pressure.
• Ability to work independently, take ownership across team boundaries, continuously improve systems, and collaborate effectively.

Nice-to-haves

• Storage experience for AI/ML workloads, including checkpointing, dataset streaming, model-weight distribution, GPU-adjacent data locality, or GPUDirect Storage.
• Kubernetes storage internals, including CSI drivers, PV/PVC lifecycles, StatefulSets, and local persistent volumes.
• Bare-metal and colocation experience, including hardware selection, vendor management, firmware, and physical failure domains.
• Experience operating multi-tenant systems with isolation, fairness, and QoS requirements.
• Experience at a fast-growing cloud or infrastructure provider.

Compensation and Benefits

• Base salary range: $180,000–$260,000 USD annually.
• Equity through stock options.
• Medical, dental, and vision plans.
• Flexible paid time off.
• $1,200 home office and equipment stipend.