Principal Engineer – Scale-Up GPU Networking (HPC / AI)

Job Description:

High Performance Computing, AI and Labs is a critical element of HPE. We are focused on delivering innovative solutions that accelerate our customers’ digital transformation, enabling them to tackle their complex, and data-intensive workloads. Combining deep expertise and the development of the world’s most cutting-edge, high-performance supercomputers, is defining the next era of computing delivering valuable insight & innovation. Join us and redefine what’s next for you.

• Design and implement GPU-aware networking paths for high-bandwidth, low-latency intra-node communication.
• Develop and optimize GPU → NIC → GPU data movement, shared memory models, and DMA pathways.

• Work with NVIDIA CUDA, NVLink, NCCL, and AMD ROCm, InfinityFabric, RCCL teams to integrate and optimize scale-up communication semantics.
• Drive improvements to DMA engines, BAR mappings, ATS/IOMMU, and GPU memory registration workflows.

• Enhance and extend Libfabric, UCX, CXI, SHMEMX, OpenMPI for GPU-accelerated scale-up workflows.
• Optimize communication collectives, transport layers, and GPU-direct capabilities.

• Characterize and tune multi-NIC per socket, NUMA-zone mapping, GPU locality, CQ/queue design, and CPU/GPU topology optimization.

• Lead upstream contributions to open-source projects (OFI, UCX, OpenMPI, RCCL/NCCL enablement).
• Partner with HPC/AI ecosystem teams to shape future architectures.

• Own complex debugging across driver, runtime, GPU, kernel, and user-space boundaries.
• Develop profiling workflows using Nsight, ROCm tools, eBPF, perf, etc.

• 10–15+ years building high-performance networking, GPU, or kernel-level software.
• Deep expertise in C/C++, Linux internals, memory management, RDMA, PCIe, IOMMU, ATS, DMA engines.
• Strong understanding of CUDA, ROCm, GPU memory models, P2P, GDS (GPUDirect Storage), GDR (GPUDirect RDMA).
• Hands-on experience with MPI, SHMEM, Libfabric, UCX, or similar communication stacks.
• Proven experience driving architecture, cross-org technical decisions, and upstream contributions.
• Ability to mentor senior engineers, influence multi-team designs, and own end-to-end delivery.

• Experience with NIC architecture (CXI, RoCE, Infiniband, Slingshot, NVLink Switch).
• Experience optimizing collectives (AllReduce/AllGather) on GPUs.
• Background contributing to open-source HPC/AI libraries.
• Familiarity with HPC system architecture, NUMA tuning, and multi-accelerator systems.

Accessibility

HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form linked here.

Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.

Health & Wellbeing

We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.

Personal & Professional Development

We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.

Unconditional Inclusion

We are unconditionally inclusive in the way we work and celebrate individual uniqueness. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.

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Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.

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