СШАSenior Staff Software Engineer, Storage DPU
1 нед. назад
СШАLeadOnsite
dpustoragedistributed systemsperformance optimizationsmartnics
Senior Staff Software Engineer to develop and optimize a globally distributed, high-performance storage platform using DPUs for AI/HPC workloads.
Обязанности
- 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 '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 '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
Требования
- 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
Будет плюсом
- 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.
Условия
- Competitive compensation and equity packages
- Restricted Stock Units
- Paid time off, paid holidays & leave of absence programs
- Comprehensive health, dental & vision insurance
- Employer contributions to HSA account
- Paid parental leave
- Paid life insurance, short-term and long-term disability
- Professional development & tuition reimbursement
- Mental health & wellness support
- Commuter benefits (parking & transit)
- Cell phone stipend
- 401(k) Retirement plan with company match up to 4% of salary
- Volunteer time off
- Global travel insurance & emergency assistance
- Daily meals allowance
- Additional perks & programs specific to location
- Compensation will be paid in the range of up to $245,000 - $290,000 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicant's knowledge, education, and abilities, as well as internal equity and alignment with market data.
Другое
- is on a mission to accelerate the abundance of energy and intelligence . As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join , you join a team that is building the future, faster.
- We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI.
- We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services.
- If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at .
- 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 '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.