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 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:
Proactively dive into new feature and spec from Pre-Si and lead test strategies development, including functional validation methodology, characterization and tuning strategy, platform/component interoperability, test design, debug capability and tools/scripts, system, SLT, and manufacturing test requirement.
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, bring-up, performance/power optimization, issue debug, productization, or a related role.
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:
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.