Senior GPU System Architect

Nvidia

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Santa Clara, CA
Salary: $184,000–$287,500 / yr
Employment: Full-time
Posted: 150 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $224k

This role $236k

$154k most similar roles pay here $302k

This role pays more than 72% of similar roles. Most pay $196,750–$250,812 — the shaded band above. At the midpoint, this role pays about $236k versus about $224k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1391 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1116 roles with salary data.

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At a glance

TL;DR · Senior GPU System Architect

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As a Senior GPU System Architect, you will join the team to architect and design multi-GPU scale-up and scale-out systems for next-generation datacenter platforms supporting AI and HPC workloads. You will be responsible for defining system architectures that couple GPU compute, high-bandwidth memory, in-package interconnects, and communication fabric subsystems to ensure performance, scalability, and resilience. Your daily work involves modeling system topologies, evaluating high-speed interconnects like NVLink and Ethernet, and collaborating on RDMA-capable hardware and transport layer optimizations. You will utilize Python or SystemC for simulation and bottleneck analysis while coordinating with ASIC, compiler, and software teams. The role requires expertise in protocols such as InfiniBand, CXL, and PCIe, alongside experience in hardware-software co-design, NUMA, memory models, and multi-node rack-scale systems involving interposer and package technologies.

Skills

NVLink Ethernet InfiniBand CXL PCIe RDMA RoCE Python SystemC ASIC SoC HPC AI NUMA Hardware-Software Co-design Multi-node Fabrics

What you'll do

Architect multi-GPU system topologies for scale-up and scale-out configurations to optimize AI throughput and resilience.
Define and evaluate high-speed interconnect architectures such as NVLink and Ethernet integrated with GPU memory systems.
Design RDMA-capable hardware and develop transport layer optimizations for large-scale AI workload deployments.
Perform simulations and bottleneck analyses using system models to guide technical design trade-offs.
Execute hardware-software co-design across compute, memory, and communication layers with compiler and library teams.
Contribute to the co-design of interposers, packages, PCBs, and switches for high-density rack-scale systems.

What we're looking for

BS/MS/PhD in Electrical Engineering, Computer Engineering, or equivalent experience.
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, runtimes, and performance tuning for modern distributed computing systems.
Strong analytical and system modeling skills using Python, SystemC, or similar.
Background in AI/HPC system design, NICs/DPU architecture, chiplet interconnects, or 2.5D/3D package co-design (preferred).