NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by phenomenal technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Join our team and discover how you can develop a lasting impact on the world.
As a Sr. QA Test Developer, you will help develop the next generation of intelligent vision systems through our Metropolis platform. This platform includes an evolving ecosystem of DeepStream, Video Search and Summarization (VSS), Agentic AI and Physical AI applications. This role provides an excellent opportunity to merge thorough test engineering with AI-supported software development, revolutionizing test creation, automation and scalability. You will work alongside a versatile team using modern technologies to elevate quality across our dynamic ecosystem.
What you will be doing
- Develop and improve Python-based test frameworks and CI/CD automation for NVIDIA Metropolis, validating end-to-end IVA and video AI workflows across cloud, data center, workstation and edge platforms.
- Build and complete functional, integration, system and end-to-end validation for VSS and Metropolis applications, blueprints and workflows including multi-camera, multi-stream, multi-model and simulation-based workflows spanning video ingestion, decoding, preprocessing, inference, tracking, analytics and visualization.
- Develop validation frameworks for Video Search & Summarization (VSS), focusing on video indexing, semantic search, multimodal understanding, retrieval and summarization, while evaluating emerging Agentic AI workflows involving agents, tool calling and orchestration.
- Validate microservices-based and distributed AI architectures, covering APIs, service integration, inter-service communication, configuration, scalability, fault tolerance and end-to-end workflows.
- Deploy and validate Metropolis applications and workflows using Docker, Kubernetes and Helm while automating test execution across distributed GPU environments and edge platforms.
- Apply AI-powered tools and agents throughout the test development lifecycle to accelerate test generation, code development, test analysis, coverage improvement, log analysis, regression triage, root-cause analysis and code reviews.
- Benchmark AI pipelines and applications for accuracy, latency, efficiency, resource utilization, scalability, and reliability across different GPU configurations and deployment profiles.
- Partner with development, architecture, product, and release teams to identify quality risks, analyze sophisticated system-level issues, drive defects to resolution and communicate release readiness and quality metrics.
What we need to see
- B.Tech. Or M.Tech. In Computer Science, Computer Engineering, Information Technology, Electronics, or a related field, or comparable experience.
- 5+ years of hands-on software test development or automation experience, preferably in AI/ML, computer vision, video analytics, embedded systems or GPU-accelerated applications.
- Strong Python programming skills with experience building test frameworks, automation infrastructure, utilities and validation tools from the ground up.
- Hands-on experience bringing to bear AI-assisted development tools and AI-powered workflows for test development, code generation, test analysis, debugging, coverage improvement, or test development workflow optimization.
- Strong proficiency in Linux, including shell scripting, system-level debugging, process/resource analysis and command-line fixing.
- Hands-on understanding of AI/ML and computer vision, including concepts such as object detection, classification, tracking, video analytics and AI inference pipelines.
- Experience with CI/CD, Docker, and containerized application deployments, using tools such as Jenkins, GitHub Actions, GitLab CI, or equivalent.
- Strong understanding of test development methodologies, including test planning, test building, regression testing, defect lifecycle management, root-cause analysis, code/test coverage, and release validation.
Ways to stand out from the crowd
- Experience with NVIDIA Metropolis, DeepStream, Video Search & Summarization (VSS), TensorRT, CUDA, NGC, or AI inference technologies driven by GPUs.
- Experience evaluating real-time video streaming, intelligent video analytics, multi-stream/multi-camera workloads, or Video Search & Summarization (VSS) systems.
- Hands-on experience with Kubernetes and Helm, including deployment and validation on NVIDIA Jetson, RTX, or data center GPU platforms.
- Experience working on the development or validation of AI agents, Agentic AI workflows, multimodal AI, or Physical AI applications, including tool calling, orchestration, perception, or spatial understanding.
- Proven track record of applying AI tools or AI-powered test development agents to measurably improve test automation efficiency, coverage, quality, debugging, and regression efficiency.
With competitive salaries and a generous benefits package, we are widely considered to be one of the world’s most desirable technology employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/