Senior Software Engineer, GPU Libraries (Equity)

NVIDIA AI; Santa Clara (CA); On-site; USD 224,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a senior engineer to design, develop, and optimize GPU-accelerated software libraries. You will work on projects like nvComp, NPP, nvJPEG, nvTIFF, and more, spanning AI, data analytics, image processing, and scientific computing.

You will guide engineers, shape technical roadmaps, and contribute to performance across architectures. The role emphasizes collaboration, deep CUDA expertise, and a passion for advancing state-of-the-art computing.

Qualifications

• PhD or MSc preferred (or equivalent) in Computational Science, Computer Science, Applied Math, or related field.
• 12+ years of experience in high-performance parallel numerical applications with GPU acceleration using CUDA.
• Excellent C/C++ programming and software design skills.
• Proven leadership and successful delivery of software projects.
• Strong collaboration, communication, and documentation habits.

Responsibilities

• Develop scalable library software using modern tools and languages for numerical methods.
• Performance tuning, optimization, and benchmarking of algorithms on various architectures.
• Collaborate with product management and partners to define feature and performance roadmaps.
• Provide technical leadership and guidance to library engineers.
• Identify opportunities to improve library performance and maintainability.

Skills

CUDA parallel programming

C/C++ programming

Software design

Technical leadership

Collaboration & communication

Education

PhD or MSc in Computational Science/CS/Applied Math

Job description

NVIDIA is seeking a senior engineer to design, develop, and optimize GPU-accelerated software libraries. You will work on projects like nvComp, NPP, nvJPEG, nvTIFF, and more, spanning AI, data analytics, image processing, and scientific computing.

You will guide engineers, shape technical roadmaps, and contribute to performance across architectures. The role emphasizes collaboration, deep CUDA expertise, and a passion for advancing state-of-the-art computing.