NVIDIA has been redefining computer graphics, PC gaming, and accelerated computing for more than 25 years. Today, we are tapping into the unlimited potential of AI to define the next era of computing. As an NVIDIAN, you will address challenges spanning architecture, silicon, firmware, software, and production — and excellent judgment matters as much as technical depth!
Every major NVIDIA silicon product family—from the chips powering AI and datacentre systems to gaming, professional, embedded, and automotive platforms—passes through our productization work on its way to production. NVIDIA’s Silicon Co-Design Productization team works from pre-silicon strategy and feature development through bring-up, characterization, correlation, and optimization. Our charter spans power & performance modelling, bring up & tuning of low-power features, power & thermal controllers, and system-level optimization that ultimately shape how NVIDIA products are configured, binned, specified, and shipped.
What you'll be doing
Drive silicon power productization from pre-silicon planning through bring-up and production, including test strategy, feature readiness, characterization, and optimization.
Partner with architecture and design teams to identify improvements, validate features, and help translate them into production-ready solutions.
Correlate measured silicon behaviour with pre-silicon expectations, investigate gaps, and drive complex issues to root cause.
Build power and performance models and characterization methodologies that decide silicon binning, product specifications, productization decisions, and customer guidance.
Use AI/ML and data-driven methods to analyse characterization & telemetry data, identify anomalies & trends, and accelerate issue debug across silicon, board, power delivery, firmware, and software.
What we need to see
BS/MS in Electronics Engineering, Electrical Engineering, or a related field, or equivalent experience.
2+ years of experience in silicon bring-up, characterization, validation, productization, or a related hardware field.
Understanding of silicon power and performance, including process technology, voltage, frequency, workloads, and operating conditions.
Experience with system-level hardware debugging and interactions across silicon, board hardware, firmware & software.
Data analysis and problem-solving skills—you can turn measurements into hypotheses and design experiments to test them.
Ways to stand out from the crowd
Experience with Windows/Linux systems, low-power states, power controllers, silicon power, device physics, or power delivery.
Contributions that improved silicon bring-up, characterization coverage, pre/post-silicon correlation, debug efficiency, or productization methodology.
Experience with power modelling, silicon binning, productization.
Experience applying AI/ML to engineering workflows, such as debug assistants, characterization analytics, or automated anomaly and trend detection.
Each new process node and architecture brings power challenges that simulation alone cannot answer. This team works where architecture, design, silicon, systems, firmware, and software meet—with the opportunity to influence both how new silicon is built and how it ultimately ships. If you want to get close to new silicon, use data and AI to solve problems across engineering boundaries, and help turn first silicon into products at scale, we'd like to hear from you.
With competitive salaries, generous benefits package and an outstanding culture, we are broadly recognized as one of the technology world’s most desirable employers. We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, sex, gender, gender expression, sexual orientation, age, marital status, or disability status.
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Skills Required
- BS or MS in Electronics Engineering, Electrical Engineering, or a related field, or equivalent experience
- 2 or more years of experience in silicon bring-up, characterization, validation, productization, or a related hardware field
- Understanding of silicon power and performance, including process technology, voltage, frequency, workloads, and operating conditions
- Experience with system-level hardware debugging and interactions across silicon, board hardware, firmware, and software
- Data analysis and problem-solving skills, including designing experiments to test hypotheses
- Experience with Windows or Linux systems, low-power states, power controllers, silicon power, device physics, or power delivery
- Experience improving silicon bring-up, characterization coverage, pre- and post-silicon correlation, debug efficiency, or productization methodology
- Experience with power modeling, silicon binning, or productization
- Experience applying AI/ML to engineering workflows, such as characterization analytics or automated anomaly and trend detection
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.”









