Senior GPU-Accelerated Image & Data Libraries Engineer

NVIDIA; California (MO); On-site; USD 224,000 - 357,000; Full time

Job summary

NVIDIA is seeking a senior software engineer to design, develop, and optimize GPU-accelerated libraries such as nvComp, NPP, nvJPEG, and nvTIFF. You will contribute to projects powering AI, data analytics, image processing, and scientific simulations for customers worldwide.

The role requires 12+ years of experience in high-performance parallel numerical computing with CUDA and strong C/C++ programming, plus proven project leadership and collaborative skills.

Qualifications

• PhD or MSc in computational science, CS, applied math, or related field preferred.
• 12+ years experience developing high-performance parallel numerical apps with GPU acceleration using CUDA.
• Excellent C/C++ programming and software design skills.
• Proven experience leading and completing software development projects.
• Excellent collaboration, communication, and documentation habits.

Responsibilities

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

Skills

CUDA GPU programming

C/C++ programming

Software design

Leadership

Collaboration

Documentation

Python scripting

Education

PhD or MSc in Computational Science / CS / Applied Math or related

Tools

CI/CD

JIRA

Job description

NVIDIA is seeking a senior software engineer to design, develop, and optimize GPU-accelerated libraries such as nvComp, NPP, nvJPEG, and nvTIFF. You will contribute to projects powering AI, data analytics, image processing, and scientific simulations for customers worldwide.

The role requires 12+ years of experience in high-performance parallel numerical computing with CUDA and strong C/C++ programming, plus proven project leadership and collaborative skills.