Drimva

NVIDIA

Silicon Product Engineer – Manufacturing

$116K–$218K

US, CA, Santa Clara · Full-time · Posted Oct 28, 2026

Experience
3+ years of relevant industry experience.
Education
Bachelor’s or Master’s Degree or equivalent in Microelectronics/Electrical/Electronic/Computer Engineering, or equivalent experience.

About the job

Validates and improves SLT/BLT production diagnostics, test specifications, and manufacturing workflows for complex silicon products from post-silicon bring-up through high-volume production.

What you'll do

  • Develop closed-loop analysis of VF characteristics and POR expectations versus production screens, and support VF/VMIN/VMAX/PVT specification validation and productization.
  • Coordinate with hardware, software, and production teams to define test coverage, conditions, content, and flows for high-volume manufacturing.
  • Improve screening quality, yield, and efficiency through silicon correlation, debugging, data analytics, and process improvements.

What they ask for

  • Bachelor’s or Master’s Degree or equivalent in Microelectronics/Electrical/Electronic/Computer Engineering, or equivalent experience.
  • 3+ years of relevant industry experience in silicon post-silicon PVT validation and/or high-volume manufacturing testing for complex SoCs, GPUs, CPUs, or similar.
  • Hands-on lab experience with silicon bring-up, lab debug, shmoo analysis, VF characteristics, test content bring-up, and correlation to Si characterization, yield, and coverage optimization.
  • Experience with Python, BASH, Linux, and scripting.

Nice to have

  • Prior understanding of manufacturing test strategy for a major silicon product from early planning through ramp to QS/production.
  • Experience with GPUs, CPUs, AI accelerators, or other large-scale SoCs; multi-functional alignment on production methodologies is also valued.

Pay

116,000 USD - 189,750 USD (base salary, Level 2); 136,000 USD - 218,500 USD (base salary, Level 3)

Tools

Python, BASH, Linux, SLT, BLT, VF, VMIN, VMAX

AI focus

Deliberately use AI to drive impact while managing risk, including using AI tools to accelerate production testing and debug workflows.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 5, 2026 (UTC)