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Senior AI Verification Chip Design Engineer

Nvidia

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What verification roles keep asking for: Python (49%), Perl (29%), C++ (23%), Linux (13%) — counted across their open postings here.

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Questions you are likely to be asked

  1. Why do you want to join Nvidia?
  2. What is your experience with LLMs? Tell me one thing you learned the hard way.
  3. Tell me about a time the data was messy or wrong. What did you do?
  4. How would you explain your model's result to someone who is not technical?
  5. What would you check first if a model's accuracy dropped after going live?

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NVIDIA is seeking a visionary AI Verification Engineer to join our Chip Design Methodologies team. We’re expanding how we use AI across all our verification methods. In this role, you’ll help us figure out where AI fits best and build the 'brains' behind our tools. You’ll help us leverage AI, utilizing LLMs and machine learning—to help us debug complex chips faster and more efficiently What You’ll Be Doing AI Ecosystem Development: Build and deploy an AI verification environment, moving from manual testbenches to automated stimulus generation and coverage closure. Next-Gen Tooling: Develop and integrate AI agents and ML models directly within the verification toolchain to automate intent-to-testbench workflows. Intelligent Debugging: Create and refine AI-based debug assistants that can analyze simulation failures, categorize bugs, and suggest fixes autonomously. Methodology Innovation: Research and apply groundbreaking AI approaches (such as Reinforcement Learning or LLMs) to address the "state-space explosion" in chip verification. Collaborative Intelligence: Work with design automation teams and EDA vendors to ensure our AI solutions provide end-to-end efficiency from RTL to GDS. Leadership & Training: Act as the authority, training the broader team on how to bring to bear AI tools and "human-in-the-loop" methodologies. What We Need to See Bachelor’s Degree in Electrical Engineering, Computer Science, or equivalent experience. 8+ years of hands-on pre-silicon verification experience. Strategic Innovation: A perspective geared toward automation and a desire to redefine traditional "manual" verification workflows. Ways to Stand Out from the Crowd AI for EDA: Experience building or using AI tools specifically designed for hardware verification (e.g., automated coverage, log analysis, or bug prediction). NVIDIA houses the most forward-thinking minds in the world. Are you a creative engineer ready to build the first truly self-verifying chip environment? Come join our team and help us define the future of hardware powered by artificial intelligence.

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Listed on workday · posted 2026-09-07. ApplySarthi collects openings and links to application pages; the role is advertised by Nvidia, not by us.