Senior Math Libraries Engineer - Sparsity in AI at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI, HPCIndustries

Requirements

BS, MS or PhD degree in Computer Science, Applied Math, or related field (or equivalent experience)
6+ years of overall experience in developing, debugging and optimizing high-performance software using C++ and parallel programming; ideally for sparse linear algebra applications and using CUDA, MPI, OpenMP, or equivalent technologies
Experience with domain-specific language design and compiler optimizations, in particular sparse compilers (MLIR or TACO)
Excellent C++, Python, and CUDA programming skills
Strong collaboration, communication, and documentation habits and ideally experience with working in a globally distributed organization

Responsibilities

Design and develop a C++-based system to simplify and accelerate computing for unstructured sparsity in DL and HPC on NVIDIA GPUs
Enable the system in languages and frameworks that are more commonly used in DL, such as Python and PyTorch
Evaluate and improve the performance of the system on real-life applications
Realize opportunities to improve library quality, performance and maintainability by writing effective and well-tested code for production use
Work closely with product management and other internal and external partners to understand feature and performance requirements and contribute to technical roadmaps

Skills

Key technologies and capabilities for this role

C++PythonPyTorchCUDASparse TensorsDSLGPU ProgrammingHPCDeep LearningLinear Algebra

Questions & Answers

Common questions about this position

What experience level is required for this Senior Math Libraries Engineer role?

The role requires 6+ years of overall experience in developing, debugging, and optimizing high-performance software using C++ and parallel programming.

What are the must-have technical skills for this position?

Candidates need excellent C++, Python, and CUDA programming skills, along with experience in parallel programming using CUDA, MPI, OpenMP or equivalents, and domain-specific language design with sparse compilers like MLIR or TACO.

What education is required for this role?

A BS, MS, or PhD degree in Computer Science, Applied Math, or related field (or equivalent experience) is required.

What kind of collaboration is expected in this role?

The role involves strong collaboration, communication, and documentation habits, ideally with experience working in a globally distributed organization, and working closely with product management and internal/external partners.

What makes a candidate stand out for this position?

Stand out with a strong understanding of sparse computations in AI and HPC, experience with LLMs and deep learning frameworks, low-level GPU performance optimization, numerical linear algebra methods, and software practices like CI/CD and JIRA.

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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