Post-Silicon Validation and Methodology Engineer

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About the role

Join NVIDIA, a trailblazer at the forefront of graphics and artificial intelligence performance, efficiency, and innovation. From our roots as a groundbreaking graphic company, we have evolved into a global leader in artificial intelligence, continuously pushing the boundaries to tackle complex challenges across diverse industries. The problems that break late in a silicon program almost always break here first — at the boundary where architecture, design, firmware, and silicon meet reality in the lab. NVIDIA's Silicon Co-Design Group has an end-to-end view that almost no team anywhere can claim: from early architecture through bring-up to product release, across GPU, SoC, and CPU programs spanning Datacenter, Gaming, Robotics, Automotive, and Embedded. We are looking for a hardware engineer who builds the methods, test infrastructure, and coverage strategies that find issues before software is production-ready — and who rewires the team's workflow when the old approach isn't fast enough. What you will be doing: • Own bring-up, validation, qualification, tuning, and productization plans for next-generation silicon — from first power-on through PVT sign-off. • Partner across architecture, build, firmware, and software teams to define requirements for power management and clocking features, then drive coverage from pre-silicon through production. • Build and deploy AI assisted lab workflows. These workflows automate bring-up telemetry and silicon measurement data evaluation. They provide anomaly detection on regression results and debug-triage tooling. This tooling reduces the time from observation to root-cause hypothesis. The team runs these tools during every bring-up, not as occasional scripts. • Build the test infrastructure, characterization methodologies, and bring-up playbooks that shift post-silicon coverage left and raise velocity across programs. • Lead root-cause analysis on the hardest HW/SW interaction issues — with the measurement field, instrumentation judgment, and hypothesis rigor to close them under schedule pressure. • Identify where current processes break and redesign them: bring-up sequencing, debug forums, regression frameworks, and the lab metrics that tell us whether we are actually improving. What we need to see: • BS or MS in Electrical or Computer Engineering (or equivalent experience), plus 5+ years in silicon bring-up, validation, debug, or productization. • Deep fundamentals across digital development, microarchitecture, timing, clocking, power, noise, and control systems — and the ability to reason across the HW/SW boundary under real lab constraints. • Hands-on lab proficiency: oscilloscopes, logic analyzers, power analyzers, and the instinct to know which instrument answers which question during a bring-up. • Strong programming and scripting proficiency: Python, C/C++; experience building lab automation or test infrastructure that other specialists adopt and depend on — not just personal scripts. • Proficiency in the use of AI tools to accelerate silicon validation work — automated analysis of bring-up logs, regression data, or lab instrumentation output; anomaly detection on silicon measurement datasets; or LLM-assisted debug triage. We want to understand what you built, how you validated its trustworthiness, and where you decided the measurement had to stay manual. Ways to stand out from the crowd: • Hardware proof of crafting: debug infrastructure you built in the lab, characterization methodologies adopted across programs, build DFT feature specs or in-system test suites you developed, or margin test flows that caught issues before production. • Proficiency in bring-up experience with GPU/SoC architecture • Experience crafting or scaling in-system test and DFT features for pro

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