We are looking for a Software Test development engineer in NVIDIA’s AI SWQA team. The position is in NVIDIA AI Software Quality Assurance team that defines, develops and performs tests to validate robustness and measure the performance of NVIDIA‘s AI software and GPU Infrastructure for autonomous driving, healthcare, speech recognition, natural language processing, and a wide variety of other AI scenarios. This team collaborates with multiple AI product teams to develop new products; derive and improve complex test plans; and improve our workflow processes for a diverse range of GPU computing platforms. You should grow with being in the critical path supporting developers working for billion-dollar business lines as well as intimately understanding the values of responsiveness, thoroughness and teamwork. You should constantly foster and implement efficiency improvements across your domain. Join the team which is building software which will be used by the entire world!

What you’ll be doing:

• Work closely with global cross-functional teams to understand the test requirements and take ownership of product quality.

• Plan/design/execute/report/automate test plan/test case/test reports.

• Manage bug lifecycle and co-work with inter-groups to drive for solutions.

• Automate test cases and assist in the architecture, implementing/enabling test for CI/CD.

• In-house repro and verify customer issues/fixes.

What we need to see:

• Master or higher degree in computer science or similar.

• 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. Be familiar with python CI/CD pipeline development

• Direct development experience in AI tools/products or using AI for major features

• Good QA experience in external devices (like camera) and robotic ultrasound applications

• Good user/development experience of virtualization like VM & Docker container & k8s

• Excellent English written and oral communication skills.

• Good Deep Learning medical models training knowledge like Physical Intelligence (π)

• Proven success in leveraging AI tools to significantly improve efficiency, streamline workflows or enhance process automation.

Ways to stand out from the crowd:

• Experience in using AI to automate/implement QA end-to-end workflow

• Familiarity with NVIDIA GPU hardware products (Tesla, Tegra, DGX, etc.).

• Understanding and working knowledge with any Deep Learning medical models.

• Has basic knowledge of NVIDIA ISAAC and Omniverse platforms in robot domain

• Working knowledge of NVIDIA GPU Computing (CUDA) and CUDA libraries for Deep Learning like cuDNN