Join NVIDIA and contribute to the evolution of intelligent vision systems through our Metropolis platform. This dynamic role focuses on DeepStream, Video Search and Summarization (VSS), and Agentic AI applications. You'll integrate rigorous test engineering with AI-driven software development to enhance test creation, automation, and scalability. Collaborate with a talented team leveraging cutting-edge technologies to elevate quality across our diverse ecosystem.

NVIDIA offers a unique environment where innovation thrives. As a global leader in accelerated computing and AI, we are shaping the future of technology. Become part of a supportive and diverse team committed to making a lasting impact on the world.

Develop and refine Python-based test frameworks and CI/CD automation for NVIDIA Metropolis, ensuring end-to-end validation of IVA and video AI workflows across various platforms. Construct comprehensive functional, integration, system, and end-to-end validation for VSS and Metropolis applications, blueprints, and workflows. Create validation frameworks for Video Search & Summarization (VSS), focusing on core AI capabilities and emerging Agentic AI concepts.

Validate microservices-based and distributed AI architectures, including APIs, service integration, and scalability. Deploy and test Metropolis applications using Docker and Kubernetes, automating execution across distributed GPU and edge environments. Leverage AI-powered tools to enhance test development, code generation, and analysis throughout the lifecycle. Benchmark AI pipelines for performance and efficiency across different configurations. Partner with cross-functional teams to identify risks, resolve complex issues, and report on release readiness and quality metrics.

We seek candidates with a B.Tech. or M.Tech. in Computer Science, Engineering, IT, Electronics, or a related field, or equivalent practical experience. A minimum of 5 years in software test development or automation, ideally within AI/ML, computer vision, or video analytics, is essential. Strong Python programming skills are required, with proven experience in building test frameworks and automation infrastructure from scratch.

Proficiency in Linux and shell scripting, along with hands-on experience with AI-assisted development tools for test optimization, is crucial. A solid understanding of AI/ML concepts, such as object detection and video analytics, is necessary. Experience with CI/CD pipelines, Docker, and containerized deployments is expected. Familiarity with comprehensive test development methodologies, including defect lifecycle management and root-cause analysis, is vital for this role.