Join NVIDIA's DGX Cloud AI Efficiency Team to significantly enhance the performance, efficiency, and resilience of extensive AI workloads. You will empower AI researchers and platform teams by providing deep insights into the end-to-end performance across GPUs, networking, storage, and software. This role is crucial for optimizing scalable DGX Cloud systems and translating complex measurements into prioritized engineering actions.

Analyze the complete performance spectrum of large-scale AI workloads, encompassing compute, network, storage, and software layers. Design and execute comprehensive performance studies to establish benchmarks, identify regressions, and pinpoint bottlenecks. Develop methodologies, benchmarks, and success metrics for evaluating AI workload performance and efficiency. Utilize profiling, observability, and data analysis techniques to create actionable optimization plans from performance data. Collaborate with deep learning engineers, platform teams, and GPU architects to implement and validate performance enhancements. Clearly articulate performance findings, potential trade-offs, and recommendations to guide system and software design decisions.

Possess a BS or higher degree in computer science, computer engineering, or a related discipline, or equivalent practical experience. Demonstrate 12+ years of robust programming expertise in C++ and Python, with a proven ability to develop dependable analysis and automation workflows. Exhibit a strong understanding of operating systems, computer architecture, and distributed systems. Have hands-on experience in performance engineering, benchmarking, profiling, and optimizing complex software or systems. Showcase the ability to communicate technical insights effectively, prioritize high-impact tasks, and foster alignment across diverse teams.