Senior Architecture Energy Modeling Engineer at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Semiconductors, Artificial IntelligenceIndustries

Requirements

MS (or equivalent experience) with proven experience or PhD in related fields
6+ years of experience
Strong coding skills, preferably in Python, C++
Background in machine learning, AI, and/or statistical modeling
Background in computer architecture and interest in 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
Basic understanding of fundamental concepts of energy consumption, estimation, and low power design
Desire to bring quantitative decision-making and analytics to improve the energy efficiency of products
Good verbal/written communication and interpersonal skills

Responsibilities

Work with architects, designers, and performance engineers to develop an energy-efficient GPU
Identify key design features and workloads for building Machine Learning based unit power/energy models
Develop and own methodologies and workflows to train models using ML and/or statistical techniques
Improve the accuracy of trained models by using different model representations, objective functions, and learning algorithms
Develop methodologies to estimate data movement power/energy accurately
Correlate the predicted energy from models built at different stages of the design cycle, with the goal of bridging early estimates to silicon
Work with performance infrastructure teams to integrate power/energy models into their platforms to enable combined reporting of performance and power for various workloads
Develop tools to debug energy inefficiencies observed in various workloads run on silicon, RTL, and architectural simulators. Identify and suggest solutions to fix the energy inefficiencies
Prototype new architectural features, build an energy model for those new features, and analyze the system impact
Identify, suggest, and/or participate in studies for improving GPU perf/watt

Skills

Machine Learning

Power Modeling

Energy Modeling

GPU Architecture

RTL Simulation

Emulation

Silicon Platforms

ASIC Design

Performance Analysis

Statistical Techniques

Data Movement Analysis

Architectural Simulation

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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