Senior Engineer, GPU Performance Analysis

About NVIDIA

NVIDIA's invention of the GPU in 1995 redefined modern computer graphics, revolutionized parallel computing, and ignited the era of modern AI. Our work in GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, data centers, and cars that can perceive, learn, and understand the world. Today, we are a computing company with a strong presence in the data center, professional visualization, gaming, and automotive markets.

About the Role

We are looking for an outstanding Senior Engineer, GPU Performance Analysis to join our GPU performance team. You will be responsible for defining and developing innovative performance analysis and debug methodologies for our next generation GPU architectures, contributing to the development of our world-class GPUs. This is a key role in the GPU performance team, focusing on ensuring that NVIDIA GPUs continue to deliver industry-leading performance across various workloads. If you are passionate about GPU architectures, performance analysis, and working on ground breaking technology, we encourage you to apply!

What you'll be doing:

• Analyze performance bottlenecks on existing and future GPU architectures across various workloads (graphics, compute, AI).
• Develop and implement new methodologies and tools for GPU performance analysis, debug, and optimization.
• Collaborate with architecture, design, and software teams to understand performance implications and guide future GPU designs.
• Create, maintain, and enhance performance models and simulators.
• Develop and execute performance tests and benchmarks to validate architectural assumptions and identify performance issues.
• Drive performance regressions to closure through root cause analysis and recommendations for solutions.
• Mentor junior engineers and provide technical leadership within the team.

What we need to see:

• BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field (or equivalent experience).
• 5+ years of experience in GPU architecture, microarchitecture, or performance analysis.
• Strong understanding of GPU architecture, including experience with graphics (e.g., CUDA, OpenCL) and/or compute workloads.
• Proficiency in C++ and Python scripting.
• Experience with performance analysis tools, profilers, and simulators.
• Excellent problem-solving and debugging skills.
• Strong communication and interpersonal skills.

Ways to stand out from the crowd:

• Experience with pre-silicon performance analysis.
• Familiarity with various GPU workloads (gaming, professional visualization, HPC, AI/ML).
• Track record of technical leadership and successful project delivery.