AI Insights
NVIDIA

Architecture Energy Modeling Engineer

NVIDIA · Santa Clara, California, US
full-timejunior (1-4 yrs)Posted 25d ago
Hardware EngineeringIC2ICHybridVisa Sponsored
StackPythonC++Machine LearningStatistical ModelingVerilogRTL SimulationGPU ArchitectureASIC DesignEnergy ModelingPower AnalysisAlgorithm AnalysisArchitectural Simulation

Summary

Early-career to mid-level engineering role at NVIDIA focused on developing energy/power models for next-generation GPUs and Tegra SOCs, integrating ML/statistical techniques into architectural simulators, RTL, and emulation platforms to drive energy efficiency across the chip design lifecycle.

About the role

At NVIDIA, we pride ourselves in having energy efficient products. We believe that continuing to maintain our products' energy efficiency compared to the competition is key to our continued success. Our team is responsible for researching, developing, and deploying methodologies to help NVIDIA's products become more energy efficient; and is responsible for building energy models that integrate into architectural simulators, RTL simulation, and emulation platforms. Key responsibilities include developing techniques to model, analyze, and reduce the power consumption of NVIDIA GPUs.

 

As a member of the Power Modeling, Methodology, and Analysis Team, you will collaborate with Architects, Performance Engineers, Software Engineers, ASIC Design Engineers, and Physical Design teams to study and implement energy modeling techniques for NVIDIA's next-generation GPUs and Tegra SOCs. Your contributions will help us gain early insight into the energy consumption of graphics and artificial intelligence workloads, and will allow us to influence architectural, design, and power management improvements.

 

What you’ll be doing:

  • Work with architects and performance architects to develop an energy-efficient GPU.

  • Develop methodologies and workflows to select and run a wide variety of workloads to train models using ML and/or statistical techniques.

  • Develop methodologies to improve the accuracy of energy models under various constraints, such as, process, timing, floorplan and layout.

  • Correlate the predicted energy from models created at different stages of the design cycle, with the goal of bridging early estimates to silicon.

  • Develop tools to debug energy inefficiencies observed in various workloads run on silicon, RTL and architectural simulators. Work with architects to fix the identified energy inefficiencies.

  • Work with performance, verification and emulation methodology and infrastructure development teams to integrate energy models into their platforms.

  • Prototype new architectural features, create an energy model, and analyze the system impact.

 

What we need to see:

  • MS degree with 1+ year experience in related fields or equivalent experience

  • Strong coding skills, preferably in Python, C++.

  • Background in machine learning, AI, and/or statistical modeling.

  • Interest in computer architecture and energy-efficient GPU designs.

  • Familiarity with Verilog and ASIC design principles is a plus.

  • Ability to formulate and analyze algorithms, and comment on their runtime and memory complexities.

  • Desire to bring quantitative decision-making and analytics to improve the energy efficiency of our products.

  • Good verbal/written English and interpersonal skills.

With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you! NVIDIA is committed to fostering a diverse 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.

#LI-Hybrid

What you'll do

1Work with architects and performance architects to develop energy-efficient GPU designs
2Develop methodologies and workflows to select and run workloads for ML/statistical model training
3Improve accuracy of energy models under constraints such as process, timing, floorplan, and layout
4Correlate predicted energy across design cycle stages, bridging early estimates to silicon
5Develop tools to debug energy inefficiencies in workloads on silicon, RTL, and architectural simulators
6Integrate energy models into performance, verification, and emulation platforms
7Prototype new architectural features, create energy models, and analyze system-level impact

Requirements

MS degree with 1+ year of experience in computer architecture, hardware design, or related field
Strong coding ability in Python and/or C++ for tool and methodology development
Background in machine learning, AI, or statistical modeling for power/energy model training
Ability to analyze algorithms and reason about runtime and memory complexity
Interest in and familiarity with energy-efficient GPU design and computer architecture principles

Nice to have

Verilog
ASIC Design
GPU Architecture
RTL Simulation
Emulation Platforms
Power Management

Role overview

Role family
Hardware Engineering
Level
IC2 — ml_ai
Experience
1–4 years
Type
Individual Contributor
Remote policy
Hybrid
Visa sponsorship
Available

Tech stack analysis

LANGUAGES
PythonC++Verilog
INFRASTRUCTURE
RTL Simulation PlatformsEmulation PlatformsArchitectural Simulators
TOOLS
Energy Modeling ToolsPower Analysis ToolsASIC Design Tools

Salary estimate

$130K – $185K
AI-estimated salary range
Confidence82%
Reasoning

NVIDIA is a top-tier semiconductor company in Santa Clara, CA. For an MS-level engineer with 1+ year experience in a specialized hardware/ML role (IC2 level), total compensation typically ranges from $130K–$185K base salary. NVIDIA is known for strong RSU grants and bonuses which would significantly boost total comp, but base salary estimate aligns with Levels.fyi and Glassdoor data for similar NVIDIA roles at this level in the Bay Area.

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Green flags

5 items
Exposure to cutting-edge GPU and SOC design at one of the world's leading semiconductor companies — rare opportunity for early-career engineers.growth

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Benefits breakdown

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Hiring insights

JD quality
7/10
Urgency
medium
Autonomy
medium
Team size
medium (5-15)

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Red flags

PRO3 items
No salary range disclosed in the posting — candidates must negotiate blind or rely on external benchmarks.compensation

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Interview insights

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Career path

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Next roles
Senior Power Modeling EngineerGPU Architecture EngineerStaff ML Infrastructure Engineer

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About the company

NVIDIA is the world's leading designer of GPUs and AI computing platforms. Its chips power everything from gaming and data centers to autonomous vehicles and scientific research. With a market cap exceeding $2 trillion, NVIDIA's CUDA platform and AI accelerators have become the backbone of the global AI revolution.

HQSanta Clara, CA, USA
Build vs Maintainbuild
Cross-functionalYes