Distinguished Engineer, Production Silicon Diagnostics
$272,000–$425,500 year
On-siteSanta Clara, California, United States
Job Summary
Define and lead strategy for NVIDIA's production diagnostics, covering ATPG and memory diagnostics to accelerate yield learning, failure analysis, and product ramp. Establish a multi-functional operating model spanning design, DFT, test, product engineering, and foundry partners to drive advanced capabilities for next-generation products like backside power technology. Standardize and automate end-to-end diagnostic flows to improve repeatability and time-to-root-cause, while connecting signatures and physical FA results into a closed-loop yield-learning system. Build diagnostic-friendly design practices and integrate sophisticated debug techniques into the production platform.
Required Qualifications
- Bachelor's degree or higher in Engineering, Computer Science, or a related technical field, or equivalent experience
- 20+ overall years of experience in semiconductor engineering, production diagnostics, DFT, test, silicon debug, FA, yield learning, or related post-silicon subject areas
- 12+ years of technical leadership and people management experience, including building new engineering capabilities, mentoring senior technical talent, and scaling high-impact teams
- Deep expertise in ATPG diagnostics, memory diagnostics, production test data, diagnostic automation, and high-volume production debug or yield-learning flows
Desired Qualifications
- Demonstrated end-to-end creation and ownership of high-volume production diagnostics infrastructure for advanced-node technologies, complex SoCs, or advanced semiconductor products
- Deep understanding of how design, DFT, test, diagnostics, FA, yield, foundry, and analytical workflows connect to accelerate yield learning and address performance, reliability, and quality challenges
- Proven track record to influence diagnostic-friendly design and DFT practices, including test content, observability, controllability, debug hooks, and silicon learning requirements
- Experience connecting diagnostic signatures to physical defect mechanisms, FA outcomes, foundry learning, and measurable production yield improvement
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