GPU Software Development Engineer

About the role

Job Details:

Job Description:

About the Role

Join Intel's GPU Software Engineering Team

Help enable the next generation of graphics, media, AI, and compute experiences. As a GPU Software Development Engineer, you will work at the intersection of hardware and software, contributing to the development, validation, debugging, and optimization of GPU technologies across current and future Intel platforms.

In this role, you will collaborate closely with software, hardware, architecture, and validation teams to ensure world-class functionality, reliability, performance, and user experience. The ideal candidate will bring experience in GPU software development or validation, along with a strong foundation in post-silicon validation and complex hardware/software debugging.

What You’ll Do

Key responsibilities will include but not limited to:

- Develop, validate, debug, and optimize GPU software across graphics, media, compute, and AI workloads.

- Support post-silicon bring-up and validation activities to ensure feature functionality, platform stability, and product quality.

- Investigate and root-cause complex hardware, software, and system-level issues across GPU platforms.

- Collaborate with software, hardware, architecture, and validation teams to resolve technical challenges and improve platform readiness.

- Develop and maintain software, tests, automation, and debug tools using Python.

- Analyze and evaluate graphics, media, AI, compute, and power-management workloads on current and next-generation GPU platforms.

- Use debugging, profiling, and validation methodologies to improve functionality, reliability, performance, and power efficiency.

- Contribute to the enablement and validation of future platforms.

Behavioral traits that we are looking for:

- Problem solving: Uses structured analytical and debugging approaches to diagnose complex software and system-level issues.

- Technical curiosity: Demonstrates curiosity about how hardware, operating systems, drivers, and applications interact.

- Proactive execution: Identifies issues, investigates potential causes, and takes appropriate action rather than waiting for detailed direction.

- Collaboration: Works effectively across software, hardware, validation, and architecture teams to solve technical problems.

- Communication: Communicates technical findings, risks, and potential solutions clearly to engineering partners.

- Learning agility: Quickly learns new architectures, technologies, tools, and software environments.

- Quality mindset: Demonstrates attention to software functionality, reliability, performance, and maintainability.

- Adaptability: Effectively navigates changing requirements and technical priorities associated with new and unreleased platforms.