Senior Performance Software Engineer, Deep Learning Libraries at NVIDIA

Shanghai, China

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, TechnologyIndustries

Requirements

Masters or PhD degree or equivalent experience in Computer Science, Computer Engineering, Applied Math, or related field
2+ years of relevant industry experience
Demonstrated strong C++ programming and software design skills, including debugging, performance analysis, and test design
Experience with performance-oriented parallel programming, even if it’s not on GPUs (e.g. with OpenMP or pthreads)
Solid understanding of computer architecture and some experience with assembly programming
Ability to identify bottlenecks, optimize resource utilization, and improve throughput

Responsibilities

Writing highly tuned compute kernels to perform core deep learning operations (e.g. matrix multiplies, convolutions, normalizations)
Following general software engineering best practices including support for regression testing and CI/CD flows
Collaborating with teams across NVIDIA: CUDA compiler team on generating optimal assembly code; Deep learning training and inference performance teams on which layers require optimization; Hardware and architecture teams on the programming model for new deep learning hardware features

Skills

Key technologies and capabilities for this role

CUDAcuDNNcuBLASTensorRTCUTLASSGPU kernelsperformance tuningmatrix multiplyconvolutionsnormalizationsparallel algorithmsCI/CDregression testing

Questions & Answers

Common questions about this position

What education and experience are required for this Senior Performance Software Engineer role?

A Masters or PhD degree or equivalent experience in Computer Science, Computer Engineering, Applied Math, or related field is required, along with 2+ years of relevant industry experience.

What key skills are needed for this position?

Strong C++ programming and software design skills including debugging, performance analysis, and test design are required, along with experience in performance-oriented parallel programming and solid understanding of computer architecture with some assembly programming experience.

What is the compensation or salary for this role?

This information is not specified in the job description.

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

This information is not specified in the job description.

What experience would make me stand out as a candidate?

Experience tuning BLAS or deep learning library kernel code, CUDA GPU programming, and knowledge of numerical methods, linear algebra, LLVM, TVM tensor expressions, or TensorFlow MLIR would help you stand out.

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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