Job Description:
• Evaluating, explaining, and improving deep learning workloads for both training and inference, contributing to advancements in throughput, latency, and efficiency across NVIDIA GPU platforms.
• Collaborating across NVIDIA with researchers, engineers, and hardware specialists to recognize bottlenecks and achieve performance improvements.
• Developing production-quality software across the deep learning platform stack, from frameworks to deployment.
• Building automation and diagnostics that enable reproducible, scalable, and backend-agnostic performance improvements.
Requirements:
• 5+ years of relevant experience in deep learning, high-performance computing, or related fields.
• Master’s or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent experience).
• Extensive background in improving deep learning workloads, showcasing a deep understanding of training and inference constraints.
• Proven ability in GPU performance analysis and profiling, with hands-on experience applying advanced optimization techniques.
• Solid knowledge of computer architecture and familiarity with the fundamentals of GPU development.
• Strong programming skills in Python and C++.
Benefits:
• equity
• benefits