Expected this at Nvidia but still fumbled the opener a bit.
Structure your answer around concrete projects where you used CUDA, emphasizing the problems you solved and the trade-offs you made. Highlight your understanding of GPU architecture and how it influenced your optimization decisions. Tailor your response to NVIDIA's focus on performance and system design.
Pro tip: Quantify your impact with metrics like speedup or efficiency gains, and be ready to discuss a challenging CUDA bug you fixed—this shows depth and problem-solving skills that NVIDIA values.
Provide a brief overview of your years of experience, CUDA versions used, and types of projects (e.g., deep learning, HPC, graphics).
Choose one or two significant projects and describe the problem, your CUDA implementation, and the outcome.
Explain specific challenges like memory bottlenecks or kernel optimization, and the trade-offs you made (e.g., occupancy vs. register usage).
Share measurable results such as speedup, reduced latency, or improved throughput to demonstrate effectiveness.
Relate your experience to NVIDIA's technologies (e.g., TensorRT, cuDNN) and express enthusiasm for contributing to GPU-accelerated solutions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.