Product Manager, AI Platform Kernels and Communication Libraries at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Artificial Intelligence, SemiconductorsIndustries

Requirements

5+ years of technical PM experience shipping developer products for GPU acceleration, with expertise in HPC optimization stacks
Expert-level understanding of CUDA execution models and multi-GPU protocols, with a proven track record to translate hardware capabilities into software roadmaps
BS or MS or equivalent experience in Computer Engineering or demonstrated expertise in parallel computing architectures
Strong technical interpersonal skills with experience communicating complex optimizations to developers and researchers

Responsibilities

Architect developer-focused products that simplify high-performance inference and training deployment across diverse GPU architectures
Define the multi-year strategy for kernel and communication libraries by analyzing performance bottlenecks in emerging AI workloads
Collaborate with CUDA kernel engineers to design intuitive, high-level abstractions for memory and distributed execution
Partner with open-source communities like Triton and FlashInfer to shape and drive ecosystem-wide roadmaps

Skills

Key technologies and capabilities for this role

Product ManagementCUDAcuDNNNCCLNVSHMEMCUTLASSTritonFlashInferGPU ProgrammingKernel OptimizationAI InferenceDistributed Computing

Questions & Answers

Common questions about this position

What is the salary for this Product Manager role?

This information is not specified in the job description.

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

This information is not specified in the job description.

What skills and experience are required for this role?

Candidates need 5+ years of technical PM experience shipping developer products for GPU acceleration with expertise in HPC optimization stacks, expert-level understanding of CUDA execution models and multi-GPU protocols, a BS or MS in Computer Engineering or equivalent, and strong technical interpersonal skills.

What is the company culture like for Product Managers at NVIDIA?

The Product Management organization is a small, strong, and impactful group focused on enabling deep learning across GPU use cases and providing extraordinary solutions for developers.

What makes a candidate stand out for this position?

Stand out with a PhD in Computer Engineering or related field, contributions to performance-critical open-source projects like Triton or FlashAttention with measurable impact, experience crafting popular GitHub developer tools, or published research on GPU optimizations.

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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