Deep Learning Performance Architect at NVIDIA

Shanghai, China

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Not SpecifiedCompensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, AIIndustries

Requirements

BSc, MS or PhD in relevant discipline (CS, EE, Math, etc.)
3+ years of working experience in relevant directions (preferred)
Familiar with GPU or Accelerator-based deep learning platform and software stack
Strong background in computer architecture
Familiar with LLM or generative AI deep learning algorithms and kernel optimizations
Experience in system architecture design and performance optimization
Familiar with machine learning and deep learning frameworks

Responsibilities

Benchmark and analyze performance of various machine learning/deep learning algorithms on different GPU/NPU architectures
Develop performance models and provide performance projections for deep learning workloads on different architectures
Identify architecture, software and system performance bottlenecks and propose optimizations
Explore new features and hardware capabilities on deep learning applications

Skills

Key technologies and capabilities for this role

Deep LearningGPUNPUPerformance ModelingComputer ArchitectureLLMGenerative AIKernel OptimizationsMachine Learning FrameworksSystem ArchitectureBenchmarkingPerformance Optimization

Questions & Answers

Common questions about this position

What education is required for the Deep Learning Performance Architect role?

A BSc, MS, or PhD in a relevant discipline such as CS, EE, Math, etc., is required.

What experience is needed for this position?

3+ years of working experience in relevant directions is a plus, along with familiarity with GPU or Accelerator-based deep learning platforms, computer architecture, LLM or generative AI algorithms, kernel optimizations, system architecture design, and performance optimization.

What are the main responsibilities in this role?

The role involves benchmarking and analyzing performance of deep learning algorithms on GPU/NPU architectures, developing performance models and projections, identifying bottlenecks and proposing optimizations, and exploring new hardware features for deep learning applications.

Is this a remote position or does it require office work?

This information is not specified in the job description.

What is the salary or compensation for this role?

This information is not specified in the job description.

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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