Senior Silicon Validation and Methodology Engineer - In System Testing

Overview

• NVIDIA is a leader in accelerated computing and AI, driving breakthroughs in technology and innovation.
• Our Silicon Co-design Group focuses on developing cutting-edge silicon solutions, pushing the boundaries of performance in our products.
• We are looking for a Senior Silicon Validation and Methodology Engineer specializing in In System Testing to join our dynamic team.
• This role will be pivotal in ensuring the validation and performance of our silicon designs, working closely with various cross-functional teams to refine and improve our methodologies.
• What you'll be doing: Lead the development and execution of validation plans for in-system testing, ensuring rigorous assessment of silicon performance and reliability.
• Collaborate with design, verification, and architecture teams to define validation methodologies and criteria, integrating best practices into workflows.
• Develop automated test setups and scripts to ensure efficient and repeatable testing processes, improving coverage and accuracy.
• Analyze test results, identify anomalies, and provide actionable insights to engineering teams to drive design improvements.
• Mentor junior engineers in validation methodologies and best practices, fostering a collaborative and continuous learning environment.
• What we need to see: BS or MS in Electrical Engineering, Computer Engineering, or related field, or equivalent experience.
• 5+ years of experience in silicon validation or a related role, with a strong focus on in-system testing methodologies.
• Proficient in scripting languages such as Python or Perl, and experience with automation frameworks.
• Strong understanding of silicon architecture, hardware design, and testing methodologies.
• Excellent problem-solving skills and the ability to work effectively in cross-functional teams.
• Strong AI-enabled skills and thinking — use AI to accelerate analysis, exploration, and documentation while maintaining rigor, originality, and judgment that do not come from AI.
• Treat AI (LLMs, code assistants, intelligent search, internal copilots) as a core part of your workflow for: Automating test case generation and result analysis to expedite validation cycles.
• Using AI for intelligent data analysis and pattern recognition in silicon performance metrics.
• Enhancing documentation and knowledge sharing through AI-assisted writing tools.
• Applying AI to optimize testing methodologies based on historical test data and trends.
• Exercising judgment around AI outputs, knowing when to trust, verify, or override results provided by AI tools.
• Sharing effective AI patterns, prompts, and tools with fellow engineers to drive productivity.
• Ways to stand out from crowd: Experience with advanced testing techniques, including ATE (Automatic Test Equipment) and custom hardware setups.
• Familiarity with simulation and modeling tools in the context of silicon validation.
• Understanding of machine learning applications in silicon testing and validation processes.
• If you are energized by hard problems, real ownership, deep work, and AI-enabled engineering at scale, we'd love to talk to you.