Senior GPU Kernel Performance Lead at NVIDIA

Santa Clara, California, United States

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$224,000 – $425,500Compensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Artificial Intelligence, SemiconductorsIndustries

Requirements

PhD degree in Computer Science, Computer Engineering, Applied Math, or related field (or equivalent experience)
8+ years of relevant industry experience
Strong C++ programming and software design skills, including debugging, performance analysis, and test design
Demonstrated experience leading or managing a team related to the performance of CPUs, GPUs, or other DL accelerators

Responsibilities

Specify test cases derived from Deep Learning workloads to ensure adequate coverage across all kernels on simulation and silicon targets
Develop and use analytical models to determine performance theory
Track and report on kernel performance throughout the development lifecycle using existing infrastructure
Identify performance regressions and opportunities for peak performance, providing feedback to kernel developers

Skills

Key technologies and capabilities for this role

C++GPUPerformance AnalysisDebuggingSoftware DesignTest DesignAnalytical ModelingCycle-Accurate SimulatorscuDNNcuBLASTensorRTCUTLASS

Questions & Answers

Common questions about this position

What is the salary range for the Senior GPU Kernel Performance Lead position?

The salary range is $224,000 - $425,500 USD.

Is this position remote or does it require working from an office?

This information is not specified in the job description.

What are the key skills required for this role?

The role requires a PhD in Computer Science, Computer Engineering, Applied Math, or related field (or equivalent), 8+ years of relevant industry experience, strong C++ programming and software design skills including debugging, performance analysis, and test design, and demonstrated experience leading or managing a team related to CPU/GPU performance.

What is the team culture like at NVIDIA's Deep Learning Architecture team?

The team plays a key role in enabling breakthroughs in areas like image classification, speech recognition, natural language processing, and large language models, and helps build the real-time, cost-effective AI computing platform driving NVIDIA’s success. NVIDIA is widely considered one of the technology world’s most desirable employers.

What makes a candidate stand out for this position?

Candidates stand out with experience in analytical models and cycle-accurate HW simulators, knowledge of performance tools like Nsight or VTune, and programming experience beyond C++ including assembly, MLIR/LLVM, Python, and CUDA/OpenCL.

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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