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NVIDIA

Designs the chips and software that power AI

Senior Hardware Architect Artificial Intelligence

Hardware Systems IntegrationConfirmed live in the last 24 hours
Full-timeNo salary listedIn-person

About the Job

Requirements

  • A B.A., M.Sc., or Ph.D. in Computer Engineering, Electrical Engineering, Computer Science, or equivalent experience.
  • At least 6 years of experience in hardware, firmware, or system architecture for NIC, switch, DPU, CPU, or SoC systems.
  • Experience with architectural workflows defining microarchitecture specifications, performance models, or architecture decision documents.
  • Ability to understand and adopt agentic AI workflows and tools such as Claude, Codex, or Cursor.
  • Ability to judge AI output quality for architecture tasks and suggest validation and fix strategies to improve it.
  • Ability to explain AI flows to architects without AI engineering expertise and distill hardware architecture trade-offs for engineers without architectural backgrounds.
  • A consistent track record of driving adoption of new tools or processes across a technical organization.

Responsibilities

  • Define the roadmap for AI-driven architecture flows, including data collection, modelling, and micro-architecture agents that help architects explore feature options, as well as review and validation agents that set output quality standards.
  • Work with architects on difficult problems, suggest and build agentic flows to address them, and shorten the time to a good solution.
  • Partner with the AI engineering pod to translate architecture workflows into production agents, MCP integrations, and evaluation harnesses.
  • Serve as the domain authority and quality judge by recognizing excellent architecture output and verifying AI-assisted flows meet that standard.
  • Drive adoption by working with networking architects, running beta cycles, closing feedback loops, and facilitating widespread use of tools and methods.
  • Represent the team's technical direction to senior architecture and product leadership.

Desired Qualifications

  • Hands-on experience applying large language models, agents, or prompt-plus-code pipelines to real engineering work.
  • Expertise with high-speed networking silicon, including InfiniBand, Ethernet, switch fabric architecture, and NIC/RDMA subsystems.
  • Familiarity with large language models, including transformer architecture (attention, MLP, MoE) and LLM training/inference parallelism (DP, PP, EP, TP).
  • Background defining and measuring engineering productivity metrics to make the impact of work visible to leadership.
  • A track record of shipping internal platform tools or developer-experience infrastructure at scale.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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