# Senior GPU Networking Architect

**Company**: NVIDIA
**Location**: Zurich
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Salary**: $120k-$180k
**Category**: Engineering
**Industry**: Technology

**Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/Switzerland-Zurich/Senior-GPU-Networking-Architect_JR2015083?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_ad70d186-b59

## Description

We are looking for a Senior GPU Networking Architect to join our networking software group, bringing strong GPU architecture and programming skills to build and improve GPU communication kernels. This role links GPU computing with networking by making sure communication primitives are carefully developed alongside GPU hardware capabilities.

As a Senior GPU Networking Architect, you will be responsible for building, implementing, and optimizing GPU communication kernels that underpin collective and point-to-point operations in large-scale AI systems. You will leverage deep knowledge of GPU architecture,thread scheduling, memory hierarchy, execution pipelines,to improve kernel efficiency, minimize latency, and overlap computation with communication.

Key responsibilities include:

- Building, implementing, and optimizing GPU communication kernels that underpin collective and point-to-point operations in large-scale AI systems.

- Leveraging deep knowledge of GPU architecture,thread scheduling, memory hierarchy, execution pipelines,to improve kernel efficiency, minimize latency, and overlap computation with communication.

- Developing GPU-resident communication primitives and device-side APIs that enable fine-grained, kernel-initiated data movement across nodes and accelerators.

- Profiling and tuning GPU kernels end-to-end, identifying bottlenecks at the intersection of compute, memory, and network, and driving targeted optimizations.

- Collaborating with network software, hardware, and AI framework teams to co-design communication strategies that align with GPU execution patterns and emerging model architectures.

- Building proofs-of-concept, conducting experiments, and performing quantitative modeling to evaluate and validate new communication strategies before committing them to production.

To succeed in this role, you will need:

- 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 programming
- GPU architecture
- GPU communication kernels
- GPU data movement mechanisms
- GPU performance profiling

### Nice to have
- Systems-level C/C++ development
- Performance-critical environments
- GPU-resident communication primitives
- Device-side APIs
- Fine-grained data movement

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