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NVIDIA is an industry leader with groundbreaking developments in High-Performance Computing, Artificial Intelligence, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is seeking senior engineers who are mindful of performance analysis and optimization to help us squeeze every last clock cycle out of all facets of Deep Learning such as training and inferencing, one of today's most important workloads in the world. If you are unafraid to work across all layers of the hardware/software stack from GPU architecture to Deep Learning Framework to achieve peak performance, we want to hear from you! This role offers an opportunity to directly impact the hardware and software roadmap in a fast-growing technology company that leads the AI revolution while helping deep learning users around the globe enjoy ever-higher training speeds. In this position, you will understand, analyze, profile, and optimize deep learning workloads on state-of-the-art hardware and software platforms. You will build tools to automate workload analysis, workload optimization, and other critical workflows. Collaboration with cross-functional teams will be essential to analyze and optimize cloud application performance on diverse GPU architectures. You will identify bottlenecks and inefficiencies in application code and propose optimizations to enhance GPU utilization. Driving end-to-end platform optimization from a hardware level to the application and service levels will be a key responsibility. Additionally, you will design and implement performance benchmarks and testing methodologies to evaluate application performance, providing guidance and recommendations on optimizing cloud-native applications for speed, scalability, and resource efficiency. Sharing knowledge and best practices with domain expert teams as they transition applications to distributed environments will also be part of your role.
