Deep Learning Performance Architect - Perf Tools at NVIDIA

Shanghai, China

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, High Performance Computing, SemiconductorsIndustries

Requirements

BS+ in Computer Science, Electronic Engineering or related (or equivalent experience)
4+ years of software development
Strong software skills in design, coding (C++ and Python), analytical and debugging in low-level programs
Strong grasp of computer architecture (pipelines, memory hierarchies) and operating system fundamentals
Experience with performance modeling, architecture simulation, profiling, and analysis
Self-starter who thrives in dynamic environments and manages competing priorities effectively

Responsibilities

Architect Performance Tooling: Develop infrastructure tools/libraries for GPU performance analysis, visualization, and automated workflows used across GPU SW/HW development life cycle
Unlock Architectural Insights: Analyze GPU workloads to identify bottlenecks and define new hardware profiling features that enhance perf debug and profiling capabilities
AI-Powered Automation: Build AI/ML-driven tools to automate performance analysis, generate perf optimization guidance, and improve user experience of profiling infrastructure
Cross-Stack Collaboration: Partner with kernel developers, system software teams, and hardware architects to support performance study, improve CUDA software stack, and co-design performance-centric solutions for current and next-generation GPU architecture

Skills

Key technologies and capabilities for this role

C++PythonComputer ArchitecturePerformance ModelingArchitecture SimulationProfilingCUDAGPUAI/MLDebuggingOperating Systems

Questions & Answers

Common questions about this position

What education and experience are required for this Deep Learning Performance Architect role?

A BS+ in Computer Science, Electronic Engineering or related (or equivalent experience) and 4+ years of software development experience are required.

What key technical skills are needed for this position?

Strong software skills in design, coding (C++ and Python), analytical and debugging in low-level programs are required, along with a strong grasp of computer architecture (pipelines, memory hierarchies) and operating system fundamentals, plus experience with performance modeling, architecture simulation, profiling, and analysis.

Is this a remote position, or does it require working in an office?

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.

What makes a candidate stand out for this Deep Learning Performance Architect position?

Candidates stand out with experience building performance debugging and analysis tools on silicon and simulators, familiarity with CUDA System Software Stack, GPU performance profiling tools like Nsight Systems or Compute, and practical AI/ML-based projects for code generation or automated analysis.

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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