Senior Staff Software Engineer, Storage DPU

About the Role

Crusoe is building a globally distributed, high-performance storage platform purpose-built for AI/HPC workloads.

We're looking for a Senior Staff Software Engineer to own how Crusoe's storage stack uses DPUs (NVIDIA BlueField-class SmartNICs) and related hardware offload to move that work off the host CPU and onto purpose-built silicon. This is a software-defined-acceleration role sitting at the boundary between our storage data plane and the physical network/compute fabric — you'll design and build the control-plane and data-plane software that lets Crusoe's storage stack take advantage of hardware offload for NVMe-oF, RDMA, and related protocol-level work, in close partnership with the Storage Networking and Compute/Hypervisor teams.

You will set technical direction for DPU/offload strategy within Storage, work hands-on across the host/DPU boundary, and partner closely with the storage-networking, and Compute hypervisor-level DPU function.

What You'll Be Working On

• Hardware Offload for Storage Protocols: Design and implement software that offloads storage-protocol work (NVMe-oF target/initiator processing, encryption, compression, checksums, erasure coding) from the host CPU onto DPUs/SmartNICs, measurably reducing host CPU overhead per I/O.
• Control-Plane Integration: Build and maintain the integration between Crusoe's storage orchestration layer and DPU-resident services, including provisioning, health/telemetry, and failover of offload paths.
• Cross-Team Fabric Work: Partner with the storage-networking function on the RDMA/RoCE and NVMe-oF fabric work that DPU offload depends on, and with Compute/Hypervisor on SR-IOV/vDPA-based virtualization of DPU resources for multi-tenant isolation.
• Vendor Framework Integration: Evaluate and integrate vendor DPU SDKs and frameworks (e.g., NVIDIA DOCA, BlueField SNAP) to accelerate storage virtualization (virtio-blk/virtio-fs offload) without building bespoke firmware from zero.
• Observability & Testing: Build monitoring and testing infrastructure specific to DPU-offloaded paths — this is new surface area for the team, and strong tooling here determines how fast we can safely iterate.
• Performance Benchmarking: Drive IOPS/throughput/latency benchmarking and quantify host-CPU-cycles-saved per offloaded operation, to make the case for where offload investment pays off next.
• Technical Point of Contact: Serve as the storage team's technical lead on DPU-related architecture decisions, including with hardware vendors.

What You'll Bring to the Team

• 10+ years of professional software engineering experience, with deep, hands-on systems-level programming (C, C++, or Rust) and production experience in Go, Java, or similar for control-plane/orchestration work.
• Direct, hands-on experience with SmartNICs/DPUs in production — NVIDIA BlueField (2/3), Intel IPU, ConnectX 6/7, or comparable hardware-offload platforms.
• Working knowledge of NVMe-oF, RDMA/RoCE/InfiniBand, and the storage protocols DPU offload commonly accelerates.
• Experience with kernel-bypass and hardware-offload techniques: SR-IOV, vDPA, virtio (virtio-blk/virtio-fs), XDP/eBPF, or DPDK/SPDK.
• Strong Linux systems background — kernel internals, device drivers, I/O subsystems — sufficient to debug issues that span host, hypervisor, and DPU.
• Experience contributing to at least one of file, block, or object storage systems, and genuine comfort working alongside (not reimplementing) mature storage data planes.
• Demonstrated ability to work across team boundaries — this role sits at the intersection of Storage, Networking, and Compute, and will not succeed as a purely heads-down IC function.
• Bachelor’s degree or higher in Computer Science, Engineering, or related discipline.

Bonus Points

• Direct experience with NVIDIA DOCA, BlueField SNAP, or other DPU-native storage-virtualization frameworks.
• Experience building or operating NVMe-oF target/initiator offload in production.
• Prior work on multi-tenant hardware isolation (SR-IOV/vDPA) in a cloud or hyperscale environment.
• Familiarity with GPUDirect Storage or other GPU-to-storage data-path optimizations relevant to AI/HPC workloads.
• Open-source contributions to DPDK, SPDK, Open vSwitch, or similar projects.