Senior GPU Networking Architect

NVIDIA
Zurich
Workplace: OnsiteFull timeFunction: Solutions Engineering & Sales EngineeringExperience: 5+ yearsEducation: mastersSkills: ["Collaboration","Problem-solving","Communication"]

Senior GPU Networking Architect responsible for building and optimizing GPU communication kernels, bridging GPU compute with networking for large-scale AI systems. You’ll design GPU-resident primitives, profile kernels, collaborate across networks, hardware, and AI frameworks, and evaluate new strategies through proofs-of-concept and quantitative modeling to advance GPU-aware networking for next-generation AI workloads.

Loading

Loading job details...

Preparing the role view and application actions.

FursaFursa
NVIDIA
NVIDIA
4 months ago

Senior GPU Networking Architect

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 8 minutes agoStatus: Live

Job Summary

Senior GPU Networking Architect responsible for building and optimizing GPU communication kernels, bridging GPU compute with networking for large-scale AI systems. You’ll design GPU-resident primitives, profile kernels, collaborate across networks, hardware, and AI frameworks, and evaluate new strategies through proofs-of-concept and quantitative modeling to advance GPU-aware networking for next-generation AI workloads.
Location: Zurich
Workplace: Onsite
Employment Type: Full time
Job Function: Solutions Engineering & Sales Engineering

Key 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.

Key Requirements

  • •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
Experience:5+ yearsHigh-performance computingGPU computingCUDA
Education:Master's in Computer Science or related
Skills:CollaborationProblem-solvingCommunication
Tech Stack:CUDAC++Nsight ComputeNsight SystemsGPUDirect RDMAGPU-DirectNCCLNVSHMEMTensorRTPyTorchLLMVLLMPCIeInfiniBandNVLinkNVSwitch

Company Brief

NVIDIA
Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.
Industry: Electronics Manufacturing
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Santa Clara, United States
Founded: 1993
Glassdoor
Glassdoor: 4.3
WebsiteLinkedInGlassdoor