NVIDIA is hiring a Senior Software Test Development Engineer to join our AI Software Quality Assurance (AI SWQA) team. In this role, you will be responsible for validating robustness and measuring the performance of NVIDIA's AI software and GPU infrastructure. Your work will directly impact critical AI applications in autonomous driving, healthcare, speech recognition, natural language processing, and other advanced scenarios.
What You'll Do
- Work closely with global cross-functional teams to understand test requirements and take ownership of product quality.
- Plan, design, execute, report, and automate test plans, test cases, and test reports.
- Manage the bug lifecycle and collaborate with inter-groups to drive for solutions.
- Automate test cases and assist in the architecture, implementing, and enabling tests for CI/CD.
- Reproduce and verify customer issues and fixes in-house.
What We're Looking For
- A Master's or higher degree in computer science or a similar field.
- 5+ years of software quality assurance or test automation background with knowledge of test infrastructure and strong analysis skills.
- UNIX/Linux administrator and troubleshooting experience.
- Good Python software development or test development skillset, including familiarity with Python CI/CD pipeline development.
- Direct development experience in AI tools/products or using AI for major features.
- Good QA experience with external devices (like cameras) and robotic ultrasound applications.
- Good user/development experience with virtualization like VM, Docker container, and k8s.
- Excellent English written and oral communication skills.
- Microcontroller programming or developing background.
- Proven success in leveraging AI tools to significantly improve efficiency, streamline workflows, or enhance process automation.
Nice to Have
- Experience in using AI to automate or implement QA end-to-end workflows.
- Familiarity with NVIDIA GPU hardware products (Tesla, Tegra, DGX, etc.).
- Understanding and working knowledge with any Deep Learning Frameworks and Inference models.
- Basic knowledge of low latency inference.
- Working knowledge of CUDA libraries for Deep Learning like cuDNN and TRT-LLM.
Technical Stack
- Python
- UNIX/Linux
- CI/CD
- AI tools/products
- Virtualization (VM, Docker, k8s)
- Deep Learning Frameworks
- CUDA libraries (cuDNN, TRT-LLM)
- NVIDIA GPU hardware (Tesla, Tegra, DGX)
Team & Environment
You will be part of NVIDIA's AI SWQA team, collaborating with multiple AI product teams.
NVIDIA is an equal opportunity employer.
