Senior CUDA Software Engineer - Warp HPC & Python

NVIDIA; California (MO); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits package

Job summary

NVIDIA Warp team is seeking a hands-on senior engineer to expand Warp adoption across science and engineering. You will own the end-to-end technical path for Warp integration, turning CUDA features into supported Warp functionality and collaborating with internal teams to enable production use on NVIDIA GPUs.

The role emphasizes performance optimization, portable APIs, and building reusable components for simulation, optimization, and AI workloads, with equity and benefits in the package.

Qualifications

• BS/MS/PhD in a computational science/engineering field or related area
• 8+ years of relevant software development experience
• Significant CUDA C++ software experience for NVIDIA GPUs, incl. parallel execution & memory behavior
• Experience building computational software for research or production

Responsibilities

• Own end-to-end technical path for adopting Warp in computational applications, from native C++/CUDA changes through production integration
• Turn new CUDA features and models into Warp functionality that improves performance & portability across NVIDIA platforms
• Partner with internal engineering teams to integrate Warp into their software and support production adoption
• Select and integrate CUDA-X libraries and other computing tech to address recurring gaps
• Design and implement Warp improvements, APIs, programming abstractions, and reusable building blocks for simulation, optimization, and AI workloads

Skills

CUDA C++

Python

GPU performance tuning

CUDA programming

Education

BS/MS/PhD in computational science/engineering or related field

Tools

CUDA-X libraries

Job description

NVIDIA Warp team is seeking a hands-on senior engineer to expand Warp adoption across science and engineering. You will own the end-to-end technical path for Warp integration, turning CUDA features into supported Warp functionality and collaborating with internal teams to enable production use on NVIDIA GPUs.

The role emphasizes performance optimization, portable APIs, and building reusable components for simulation, optimization, and AI workloads, with equity and benefits in the package.