NVIDIA has continuously reinvented itself. Our invention of the GPU sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. Today, research in artificial intelligence is booming worldwide, which calls for highly scalable and massively parallel computation horsepower that NVIDIA GPUs excel.NVIDIA has continuously reinvented itself. Our invention of the GPU sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. Today, research in artificial intelligence is booming worldwide, which calls for highly scalable and massively parallel computation horsepower that NVIDIA GPUs excel.
We are seeking a GPU System Architect who will architect and design multi-GPU scale-up and scale-out systems for next-generation datacenter platforms for AI and HPC. The architect in this role will explore and define system architectures that tightly couple GPU compute, high-bandwidth memory, in-package interconnects and GPU-to-GPU communication fabric subsystems to deliver industry-leading AI performance, scalability and resilience. The ideal candidate combines deep hands-on system-level fabric/networking architecture experience, and practical hardware-software co-design expertise.
What you will be doing:
Architect multi-GPU system topologies for scale-up and scale-out configurations, balancing AI throughput, scalability, and resilience.
Define, modify and evaluate future architectures for high-speed interconnects such as NVLink and Ethernet co-designed with the GPU memory system.
Collaborate with other teams to architect RDMA-capable hardware and define transport layer optimizations for GPU-based large scale AI workload deployments.
Use and modify system models, perform simulations and bottleneck analyses to guide design trade-offs.
Work with GPU ASIC, compiler, library and software stack teams to enable efficient hardware-software co-design across compute, memory, and communication layers.
Contribute to interposer, package, PCB and switch co-design for novel high-density multi-die, multi-package, multi-node rack-scale systems consisting of hundreds of GPUs.
What we need to see:
BS/MS/PhD in Electrical Engineering, Computer Engineering, or equivalent area.
8 years or more of relevant experience in system design and/or ASIC/SoC architecture for GPU, CPU or networking products.
Deep understanding of communication 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 and runtimes, and performance tuning for modern distributed computing systems.
Strong analytical and system modeling skills (Python, SystemC, or similar).
Excellent cross-functional collaboration skills with silicon, packaging, board, and software teams.
Ways to stand out from the crowd:
Background in system design for AI and HPC.
Experience with NICs or DPU architecture and other transport offload engines.
Expertise in chiplet interconnect architectures or multi-node fabrics and protocols for distributed computing.
Hands-on experience with interposer or 2.5D/3D package co-design.
#LI-Hybrid
Skills Required
- 8 years or more of relevant experience in system design and/or ASIC/SoC architecture for GPU, CPU or networking products
- Deep understanding of communication 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
- Experience with hardware-software interaction, drivers and runtimes
- Strong analytical and system modeling skills (Python, SystemC, or similar)
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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