Join NVIDIA's GPU Performance Infrastructure team and contribute to a new era of validating and delivering next-generation GPU architectures. This role offers a unique opportunity to influence the entire GPU performance verification lifecycle, from initial modeling to post-silicon validation. You will be instrumental in shaping the future of computing by driving powerful performance through innovative infrastructure and workflows.

NVIDIA is a leader in transforming computer graphics, PC gaming, and accelerated computing. We are at the forefront of AI, defining the next era of computing where GPUs are the core intelligence for computers, robots, and self-driving cars. As an NVIDIAN, you will be part of a diverse and supportive environment, empowered to do your best work and make a lasting global impact.

Design and maintain automated chip performance verification pipelines, including workload trace generation, multi-target test execution, and results correlation. Develop robust performance monitoring and measurement infrastructure for consistent observability across simulation, emulation, and silicon environments. Build scalable data pipelines and full-stack dashboards for ingesting, visualizing, and comparing performance metrics. Collaborate with GPU architects to create infrastructure for performance verification of upcoming architectures. Work closely with hardware teams to automate and accelerate verification workflows, facilitating faster GPU build iterations. Provide insights to GPU architects, enabling them to understand current performance and model future industry-leading performance. Enhance the daily workflows of chip architects, contributing to the development of the next generation of GPUs.

A Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field, or equivalent practical experience, is required. Candidates should possess a minimum of 3 years of software engineering experience, with a strong emphasis on C++ and Python for tooling and automation. Solid understanding of computer science fundamentals, including data structures, algorithms, object-oriented programming, and system design is essential. Full-stack development experience, including backend technologies like Java or Python, REST APIs, and modern frontend frameworks such as React or Ember for data-intensive applications, is considered a significant advantage. Proficiency in database systems like SQL and MongoDB for metrics storage and querying is beneficial. Hands-on experience with version control (Git), CI/CD practices, containerization (Docker), orchestration (Kubernetes), and messaging systems like RabbitMQ is advantageous. Familiarity with GPU, CPU, or SoC chip performance verification is a plus. Exposure to AI development tools is also desirable.