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Senior GPU Networking Architect

NVIDIA· Architect
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Zurichfull-timePosted 7mo ago

About the role

Senior role in NVIDIA’s networking software group focused on building and optimizing GPU communication kernels for large-scale AI systems, linking GPU computing with networking to enable efficient, GPU-aware data movement across nodes and accelerators.

Responsibilities

  • Build, implement, and optimize GPU communication kernels that underpin collective and point-to-point operations in large-scale AI systems.
  • Leverage deep knowledge of GPU architecture—thread scheduling, memory hierarchy, execution pipelines—to improve kernel efficiency, minimize latency, and overlap computation with communication.
  • Develop GPU-resident communication primitives and device-side APIs that enable fine-grained, kernel-initiated data movement across nodes and accelerators.
  • Profile and tune GPU kernels end-to-end, identifying bottlenecks at the intersection of compute, memory, and network, and driving targeted optimizations.
  • Collaborate with network software, hardware, and AI framework teams to co-design communication strategies that align with GPU execution patterns and emerging model architectures.
  • Build proofs-of-concept, conduct experiments, and perform quantitative modeling to evaluate and validate new communication strategies before production.
  • Contribute to the evolution of programming models that expose GPU-aware networking capabilities to application developers.

Qualifications

  • 5+ years of hands-on CUDA programming, including writing and optimizing non-trivial GPU kernels.
  • M.Sc. or equivalent experience in computer science, computer engineering, or a closely related field.
  • Strong understanding of GPU architecture fundamentals: warp scheduling, shared memory, L2 cache, memory coalescing, occupancy tuning, and asynchronous execution.
  • Experience with systems-level C/C++ development in performance-critical environments.
  • Familiarity with GPU data movement mechanisms such as GPUDirect RDMA and GPU-initiated communication.
  • Ability to read and reason about GPU performance profiles (e.g., Nsight Compute, Nsight Systems) and translate observations into actionable optimizations.
  • Strong collaboration skills in a multi-national, interdisciplinary environment.

Skills

Required:CUDA programmingC/C++GPU architectureGPU data movement mechanisms
Last updated Oct 23, 2026. Interested? Apply on the company site.

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NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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