NVIDIA’s Silicon Co-Design Group sits at the crossroads of architecture, silicon, systems, and manufacturing where first-principles thinking and engineering judgement at the highest level translate directly into product outcomes at scale. Our team gets every GPU, SoC, and CPU silicon program from first power-on to high-volume production.
We are hiring a Senior System Level Testability Engineer to join our Test, Manufacturability, Reliability & Quality (TMRQ) team. You will define and drive system-level diagnostics, verification, and post-silicon validation for some of the industry's most complex semiconductor chips. You have experience defining SLT production coverage specifications, validating them on silicon, and translating product requirements into effective test coverage. Hands-on lab experience with system-level post-silicon bring-up and debug is required.
The exceptional hire continuously improves production workflows and products cross-functionally and uses AI deliberately to drive impact while managing risk.
What you will be doing:
Partner with other HW engineering teams such as Silicon design and characterization teams, IO design and validation teams, system feature owners, Production Operations to determine coverage needs and constraints to resolve appropriate test conditions, diagnostic contents for production line screen.
Define requirements to SW and board teams to support and improve test infrastructure, debug knobs and data collection features needed to support manufacturing screens effectively.
Co-design testability scope and coverage enhancements for Nvidia's manufacturing testing with a focus on system-level test
Prototype and fine-tune speed, power, performance, yield, and quality on pre-production silicon.
Co-design , develop, and maintain system-level testing flows and incorporate takeaways from silicon and correlate to design.
Define procedures to facilitate correlation activities between production testing infrastructure and internal diagnostic programs
Support customer facing and quality teams during customer escalations to understand the issue and fix gaps identified in coverage.
Create key performance indicators used to evaluate the effectiveness of the new tests.
Initiate and drive process improvements and preventative actions through root cause analysis.
What we need to see:
Bachelor’s or Master’s Degree or equivalent in Microelectronics/Electrical/Electronic/Computer Engineering or equivalent experience.
8+ years of relevant industry experience
Strong EE fundamentals, knowledgeable in digital design, signal integrity, statistics, timing analysis, fault analysis, sampling and computer architecture.
Strong data analysis skills. Prior experience working with volume data is a plus.
Validated hands-on lab experience with silicon bring-up and lab debug.
Good understanding of firmware/driver structures and their interaction with HW.
Problem-solving, debugging, analysis, and logical reasoning skills with examples to prove it
Experience with Python, BASH, Linux
Ways to stand out above the crowd:
Exceptional work ethic and up-leveling discussions to solve the real problems
Native use of AI to accelerate production testing and debug workflows
Battle-tested on GPUs, CPUs, AI accelerators, or other large-scale SoCs — you’ve seen firsthand how complexity at scale reshapes the failure landscape and demands a different level of engineering intuition.
Co-designed or led multi-functional alignment on production methodologies adopted by other teams in an HVM environment.
#LI-Hybrid
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Bachelor's or Master's degree in Microelectronics, Electrical Engineering, Electronic Engineering, Computer Engineering, or equivalent experience
- 8+ years of relevant industry experience
- Strong electrical engineering fundamentals, including digital design, signal integrity, statistics, timing analysis, fault analysis, sampling, and computer architecture
- Strong data analysis skills
- Hands-on laboratory experience with silicon bring-up and lab debugging
- Understanding of firmware and driver structures and their interaction with hardware
- Problem-solving, debugging, analysis, and logical reasoning skills
- Experience with Python, Bash, and Linux
- Experience with GPUs, CPUs, AI accelerators, or large-scale SoCs
- Experience co-designing or leading multifunctional production methodologies in a high-volume manufacturing environment
- Use of AI to accelerate production testing and debugging workflows
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
