GPU System Architect

Job Title

GPU System Architect

Role Summary

Design and architect multi-GPU scale-up and scale-out datacenter systems for AI and HPC, focusing on compute, memory, interconnects, and GPU-to-GPU communication fabrics. The role works across ASIC, packaging, board and software teams to deliver scalable, high-performance, resilient systems.

Experience Level

Senior. The posting specifies 2+ years of relevant experience in system design and/or ASIC/SoC architecture; the title indicates senior-level responsibilities.

Responsibilities

Main responsibilities include system-level architecture, modeling, and cross-team co-design:

• Architect multi-GPU topologies for scale-up and scale-out configurations, optimizing throughput, scalability, and resilience.
• Define and evaluate high-speed interconnect architectures (e.g., NVLink, Ethernet) co-designed with GPU memory systems.
• Collaborate to architect RDMA-capable hardware and define transport-layer optimizations for large-scale GPU deployments.
• Develop and modify system models, run simulations, and perform bottleneck analyses to guide trade-offs.
• Work with GPU ASIC, compiler, library, and software teams to enable hardware-software co-design across compute, memory, and communication layers.
• Contribute to interposer, package, PCB, and switch co-design for high-density multi-die, multi-package, rack-scale systems.

Requirements

Must-have technical skills and experience. Educational degrees are listed separately below.

• 2+ years of relevant experience in system design and/or ASIC/SoC architecture for GPU, CPU, or networking products.
• Deep understanding of interconnect protocols such as NVLink, Ethernet, InfiniBand, CXL, and PCIe.
• Experience with RDMA/RoCE or InfiniBand transport offload architectures.
• Proven ability to architect multi-GPU/multi-CPU topologies with awareness of bandwidth scaling, NUMA, memory models, coherency, and resilience.
• Experience with hardware-software interaction, drivers, runtimes, and performance tuning for distributed systems.
• Strong analytical and system-modeling skills (Python, SystemC, or similar).
• Excellent cross-functional collaboration skills with silicon, packaging, board, and software teams.

Nice-to-have:

• Background in system design for AI and HPC.
• Experience with NICs, DPU architecture, or other transport offload engines.
• Expertise in chiplet interconnects, multi-node fabrics, or protocols for distributed computing.
• Hands-on experience with interposer or 2.5D/3D package co-design.

Education Requirements

BS, MS, or PhD in Electrical Engineering, Computer Engineering, or a related field — or equivalent practical experience.

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.