NVIDIA is building some of the most sophisticated semiconductor platforms globally, powering breakthroughs in networking, AI infrastructure, and high-performance computing. Our NICs and Switches link the largest AI clusters worldwide. Powered by the AI revolution we are changing how hardware architecture work happens!
We strive to define and implement this new agentic hardware architecture, and we are seeking an experienced architecture lead for our AI-for-Architecture team within the Networking Architecture group. You will define and promote intelligent architecture flows across our NIC and Switch architecture groups. You will develop the agents, tools, and processes that transform how architects explore design tradeoffs, and validate models and specifications. In this leadership role with org-wide scope, you will collaborate with an AI engineering team to develop flows from concept to production. You will also serve as the technical authority on what good AI architecture work means at NVIDIA.
What You'll Be Doing:
- Define the roadmap for AI-driven architecture flows — from data collection, modelling and micro-architecture agents that help architects explore feature options, to review and validation agents that set the standard for output quality.
- Work with architects on tough problems, suggest and build agentic flows to address these problems and shorten the time to a good solution.
- Partner with the AI engineering pod to translate architecture workflows into production agents, MCP integrations, and eval harnesses.
- Act as the domain authority and quality judge: recognize what excellent architecture output looks like and verify that AI-assisted flows meet that bar.
- Drive adoption: work with networking architects, run beta cycles, close feedback loops and facilitate widespread usage of our tools/methods.
- Represent the team's technical direction to senior architecture and product leadership.
What We Need to See:
- B.A, M.Sc. or Ph.D. in Computer Engineering, Electrical Engineering, Computer Science, or equivalent experience.
- 6+ years in hardware, firmware, or system architecture — NIC, switch, DPU, CPU, or SoC. Experience with architectural workflows defining microarchitecture specs, performance models, or architecture decision documents, etc..
- Ability to understand and adopt agentic AI workflows and tools (Claude/Codex/Cursor).
- Engineering committed to quality — for example, the ability to judge AI output quality for architecture tasks and suggest validation-fix strategies to improve it.
- Strong interpersonal skills — explain AI flows for architects lacking AI engineering expertise, and distill hardware architecture trade-offs for engineers without architectural backgrounds.
- Consistent track record driving adoption of new tools or processes across a technical organization.
Ways to Stand Out from the Crowd:
- Hands-on experience applying LLMs, agents, or prompt-plus-code pipelines to real engineering work.
- Expertise with high-speed networking silicon — InfiniBand, Ethernet, switch fabric architecture, NIC/RDMA subsystems.
- Familiarity with LLMs: transformer architecture (attention, MLP, MoE) and LLM training/inference parallelism (DP, PP, EP, TP).
- Background defining and measuring engineering productivity metrics — making the impact or our work visible to leadership.
- Track record shipping internal platform tools or developer-experience infrastructure at scale.
NVIDIA's architecture teams are among the most technically demanding in the industry. If you're a senior architect who has already started using AI — and wants to institutionalize that change across an entire organization — we want to hear from you.
Skills Required
- B.A, M.Sc. or Ph.D. in Computer Engineering, Electrical Engineering, Computer Science, or equivalent experience.
- 6+ years in hardware, firmware, or system architecture (NIC, switch, DPU, CPU, or SoC).
- Experience with architectural workflows defining microarchitecture specs, performance models, or architecture decision documents.
- Ability to understand and adopt agentic AI workflows and tools (Claude/Codex/Cursor).
- Engineering committed to quality; ability to judge AI output quality for architecture tasks and recommend validation/fix strategies.
- Strong interpersonal skills to explain AI flows and distill architecture trade-offs across teams.
- Consistent track record driving adoption of new tools or processes across a technical organization.
- Hands-on experience applying LLMs, agents, or prompt-plus-code pipelines to real engineering work.
- Expertise with high-speed networking silicon: InfiniBand, Ethernet, switch fabric architecture, NIC/RDMA subsystems.
- Familiarity with LLMs: transformer architecture (attention, MLP, MoE) and training/inference parallelism (DP, PP, EP, TP).
- Background defining and measuring engineering productivity metrics.
- Track record shipping internal platform tools or developer-experience infrastructure at scale.
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.”







