We are seeking a highly motivated power architect to own and advance a critical power estimation and analysis platform used to guide architecture and product decisions. This role sits at the intersection of software development, system architecture, performance modeling, and silicon analysis.
You will develop the tools and methodologies that help architects understand power-performance behavior, evaluate performance-per-watt trade-offs, model representative workloads, and correlate early estimates with silicon telemetry. The ideal candidate combines strong software engineering fundamentals with a deep curiosity about how modern SoCs consume power and deliver performance.
What you’ll be doing:
Own the architecture, development, validation, and long-term roadmap of our power estimation and analysis tools.
Develop scalable, maintainable software for estimating and analyzing power and performance across workloads, use cases, and product configurations.
Build models that translate architectural parameters, workload behavior, utilization, and telemetry into actionable power-performance insights.
Enable rapid analysis of architectural trade-offs, including performance, power, energy efficiency, thermals, and performance per watt.
Develop workload models and representative usage scenarios for early architecture exploration and product analysis.
Integrate silicon telemetry, performance counters, power measurements, and simulation data into a unified analysis framework.
Correlate pre-silicon estimates with post-silicon measurements, identify modeling gaps, and continuously improve prediction accuracy.
Create automation, visualization, and reporting capabilities that make complex analysis accessible to architects and engineering teams.
Partner with architecture, performance, power, thermal, firmware, validation, and silicon teams to define requirements and drive technical decisions.
Establish sound software-engineering practices, including modular design, code reviews, testing, documentation, version control, and release management.
Serve as the technical owner and subject-matter expert for the tool, supporting users while growing a community of contributors.
What we need to see:
A Masters in Computer Engineering, Electrical Engineering, Computer Science, or a related STEM field—or equivalent experience. 6 years of relevant work experience
Strong software development skills in Python, C++, or similar languages, with experience building production-quality engineering tools.
Understanding of computer or SoC architecture, including processors, memory systems, interconnects, accelerators, and power-management concepts.
Experience with power and performance estimation, performance modeling, architectural exploration, or system-level analysis.
Ability to evaluate architecture trade-offs using metrics such as performance per watt, energy per task, utilization, latency, throughput, and bandwidth.
Experience modeling workloads or analyzing how workload characteristics affect system power and performance.
Familiarity with silicon telemetry, performance-monitoring counters, power sensors, traces, logs, or lab measurement data.
Strong data-analysis and debugging skills, including the ability to correlate results from multiple sources and explain unexpected behavior.
Experience designing maintainable software with clear interfaces, automated testing, documentation, and reproducible workflows.
Demonstrated ability to take ownership of an ambiguous technical area and drive it from requirements through implementation and adoption. Excellent communication and collaboration skills, with the ability to translate complex technical findings into clear recommendations.
Ways to stand out from the crowd:
Experience developing power or performance models for complex SoCs, CPUs, GPUs, AI accelerators, or automotive platforms.
Knowledge of dynamic power management, DVFS, clock and power gating, thermal management, idle states, and workload-driven resource management.
Familiarity with statistical modeling, machine learning, optimization, or sensitivity analysis applied to power-performance prediction.
A track record of turning prototype scripts into reliable, widely adopted engineering platforms.
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
- Master's in Computer Engineering, Electrical Engineering, Computer Science, or related STEM field, or equivalent experience; 6 years relevant work experience
- Strong software development skills in Python, C++, or similar languages, with experience building production-quality engineering tools
- Understanding of computer or SoC architecture, including processors, memory systems, interconnects, accelerators, and power-management concepts
- Experience with power and performance estimation, performance modeling, architectural exploration, or system-level analysis
- Ability to evaluate architecture trade-offs using metrics such as performance per watt, energy per task, utilization, latency, throughput, and bandwidth
- Experience modeling workloads or analyzing how workload characteristics affect system power and performance
- Familiarity with silicon telemetry, performance-monitoring counters, power sensors, traces, logs, and lab measurement data
- Strong data-analysis and debugging skills, including correlating results from multiple sources and explaining unexpected behavior
- Experience designing maintainable software with clear interfaces, automated testing, documentation, and reproducible workflows
- Demonstrated ability to take ownership of ambiguous technical areas and drive them from requirements through implementation and adoption; strong communication and collaboration skills
- Experience developing power or performance models for complex SoCs, CPUs, GPUs, AI accelerators, or automotive platforms
- Knowledge of dynamic power management, DVFS, clock and power gating, thermal management, idle states, and workload-driven resource management
- Familiarity with statistical modeling, machine learning, optimization, or sensitivity analysis applied to power-performance prediction
- Track record of turning prototype scripts into reliable, widely adopted engineering platforms
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.”
