Join NVIDIA, a leader in transforming computer graphics, PC gaming, and accelerated computing, as we pioneer the next era of AI. We are seeking a Senior Software QA Test Developer to contribute to our innovative Metropolis platform. This role offers a unique opportunity to integrate rigorous test engineering with AI-driven software development, revolutionizing test creation, automation, and scalability. Become part of a dynamic team utilizing cutting-edge technologies to enhance quality across our expansive ecosystem.

Develop and enhance Python-based test frameworks and CI/CD pipelines to validate end-to-end intelligent video analytics (IVA) and video AI workflows across diverse platforms. Build and execute comprehensive validation for VSS and Metropolis applications, including multi-camera, multi-stream, and simulation-based scenarios. Develop validation frameworks for Video Search & Summarization (VSS), focusing on core functionalities and emerging Agentic AI workflows. Validate microservices-based and distributed AI architectures, assessing APIs, service integration, and fault tolerance. Deploy and validate applications using Docker and Kubernetes, automating test execution across distributed GPU and edge environments. Leverage AI-powered tools throughout the test development lifecycle to accelerate processes like test generation, code development, and log analysis. Benchmark AI pipelines for performance and reliability across various configurations.

A Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology, Electronics, or a related field, or equivalent experience is required. We seek candidates with over 5 years of hands-on software test development or automation experience, ideally within AI/ML, computer vision, or video analytics. Strong Python programming skills are essential for building robust test frameworks and automation infrastructure. Prior experience utilizing AI-assisted development tools for test development is highly valued. Proficiency in Linux, including shell scripting and system-level debugging, is necessary. A solid understanding of AI/ML and computer vision concepts is expected. Experience with CI/CD, Docker, and containerized deployments is required. Familiarity with test development methodologies, including test planning and defect lifecycle management, is crucial.