Role at a glance
- Job function
-
Engineering & R&D Semiconductor & Display Engineering
- Salary
- Not Disclosed
- Location
- Santa Clara, California, United States
- Employment
- Full-time
- Experience
- 5+ years with hands-on silicon: bring-up, post-silicon speed validation, frequency characterization, or timing analysis on real hardware.
- Education
- BS or MS in Electrical Engineering, Computer Engineering, or Systems Engineering, or equivalent experience.
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Role Summary
This role characterizes silicon speed and margins from first power-on through production sign-off, and correlates measured silicon behavior with pre-silicon timing analysis. It investigates failures and develops AI-enabled automation and analysis flows to support decisions across architecture, design, and product teams.
What You'll Do
- Characterize frequency, Vmin, Vmax, and timing margins across the full PVT space from first power-on through production sign-off.
- Compare pre-silicon timing analysis and critical path predictions with measured silicon, quantify model divergences, and produce...
- Trace failures to their source, including microarchitectural bottlenecks, critical paths, clocking issues, and process corners, and...
- Build and direct AI agents for automated test orchestration, data pipelines, and analysis flows.
- Surface tradeoffs for architecture, VLSI, ASIC, firmware, and product teams using characterization data.
Generated from the employer's posting. Verify important details before applying.
View full postingQualifications
Required: BS or MS in Electrical Engineering, Computer Engineering, or Systems Engineering, or equivalent experience; 5+ years with hands-on silicon in bring-up, post-silicon speed validation, frequency characterization, or timing analysis on real hardware; computer architecture fundamentals including pipeline structures, clocking, and memory hierarchies; depth in static timing analysis and critical path identification; design intuition and statistical fluency; proficiency in silicon margining, guard-banding, and PVT/binning dependencies; scripting proficiency in Python, Perl, or C/C++.
Required
- BS or MS in Electrical Engineering, Computer Engineering, or Systems Engineering, or equivalent experience.
- 5+ years with hands-on silicon: bring-up, post-silicon speed validation, frequency characterization, or timing analysis on real hardware.
- Strong computer architecture fundamentals including pipeline structures, clocking, memory hierarchies, and how microarchitectural...
- Depth in static timing analysis, critical path identification, and the ability to read and reason about timing reports at the block and...
- Enough design intuition to know what you're measuring and enough statistical fluency to know what the data means.
- Proficiency in silicon margining, guard-banding, and PVT/binning dependencies.
- Scripting depth in Python, Perl, or C/C++.
Preferred
- You've closed the prediction-to-silicon loop with a correlation methodology precise enough that other teams adopted it.
- You've traced a frequency miss to a specific critical path, microarchitectural interaction, or process corner and driven the fix all the...
- Built or deployed AI-driven flows for characterization or analysis, and can speak to both the outcome and the guardrails you put in place.
- Background is in datacenter-scale or high-performance silicon and you know how complexity at scale changes the failure landscape and...
Original job description
Content provided by the employer
Original job description
Content provided by the employer
NVIDIA silicon runs the world's AI infrastructure. The frequency it delivers across every voltage, process corner, and workload is not assumed. It is measured, correlated, and validated. This role does that work. The Silicon Co-Design Group is where architecture intent becomes silicon reality. We own the boundary between what was designed and what was built, and we are the team that knows the difference. When a program ships at frequency and at quality, this team is a reason why.
You will be the person who follows through between simulation and silicon. When the model is wrong, a frequency corner that doesn't hold, a Vmin that walks, a critical path that timing analysis missed, you find out why, and your data is what the rest of the program acts on. Architecture, design, and product teams do not guess. They use your numbers. The engineers who do this well are rare. They think like circuit designers, work like experimentalists, and reason like data scientists. If that is you, read on.
What you'll be doing:
Own silicon speed characterization from first power-on through production sign-off, covering frequency, Vmin, Vmax, and timing margins across the full PVT space.
Close the correlation gap. Tie pre-silicon timing analysis and critical path predictions to measured silicon, quantify where the model diverges from reality, and produce analysis that architecture and design can act on with confidence.
Trace failures to their source, whether a microarchitectural bottleneck, a critical path that doesn't close under voltage, a clocking issue, or a process corner the model didn't anticipate, and drive the resolution.
Own and build AI agents that work at your direction: automated test orchestration, intelligent data pipelines, and analysis flows that expand coverage and compress cycle time without sacrificing difficulty. Know where AI accelerates real work and where it introduces risk.
Sit at the decision table. Your data surfaces tradeoffs across architecture, VLSI, ASIC, firmware, and product teams. Your analysis is what settles calls.
What we need to see:
BS or MS in Electrical Engineering, Computer Engineering, or Systems Engineering, or equivalent experience.
5+ years with hands-on silicon: bring-up, post-silicon speed validation, frequency characterization, or timing analysis on real hardware.
Strong computer architecture fundamentals including pipeline structures, clocking, memory hierarchies, and how microarchitectural decisions propagate into frequency and power.
Depth in static timing analysis, critical path identification, and the ability to read and reason about timing reports at the block and chip level.
Enough design intuition to know what you're measuring and enough statistical fluency to know what the data means.
Proficiency in silicon margining, guard-banding, and PVT/binning dependencies.
Scripting depth in Python, Perl, or C/C++. You build the tools your work depends on.
Ways to stand out from the crowd:
You've closed the prediction-to-silicon loop with a correlation methodology precise enough that other teams adopted it.
You've traced a frequency miss to a specific critical path, microarchitectural interaction, or process corner and driven the fix all the way through.
Built or deployed AI-driven flows for characterization or analysis, and can speak to both the outcome and the guardrails you put in place.
Background is in datacenter-scale or high-performance silicon and you know how complexity at scale changes the failure landscape and raises the cost of being wrong.
The chips you characterize run the world's AI infrastructure. The engineers who do this work don't just report what the silicon does, they define what it can become. If that's the level you want to operate at, we want to hear from you.
With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the world’s most desirable employers in the technology field. We encourage you to join our team, which consists of some of the most hard-working people in the world working together to promote rapid growth. Are you passionate about joining an outstanding team supporting the latest in GPU and AI technology? If so, we want to hear from you.
#LI-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 136,000 USD - 218,500 USD for Level 3, and 168,000 USD - 264,500 USD for Level 4.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.About the company
NVIDIA
Large Enterprise
NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.