Manager, Performance Research and Analysis

Responsibilities

• Drive end-to-end performance strategy, characterization, test plans, and optimization for next-generation NVIDIA AI GPU clusters, focusing on large-scale distributed training and inference workloads.
• Deeply evaluate and optimize NVIDIA Networking core technologies performance, including RDMA/PRDMA, networking protocols, collective communication (NCCL), congestion control, and load-balancing algorithms.
• Work on performance research and analysis of NVIDIA DPUs and storage technologies in North-South (N-S) use cases and deployment scenarios to maximize performance and efficiency for AI inference jobs.
• Drive the strategy for performance observability and dashboards across next-generation NVIDIA data center solutions and supercomputers by leveraging scalable, streamlined telemetry pipelines to build performance dashboards and automated analytics based on real-time performance metrics across NICs, Switches, GPUs, and…
• Perform deep root-cause analysis (RCA) on complex multi-node performance bottlenecks, driving actionable mitigation plans across hardware, firmware, and software teams.
• What we need to see
• B.Sc. or M.Sc. in Computer Science, Computer Engineering, Software Engineering, or equivalent technical experience.
• 8+ overall years of experience and deep expertise in High Performance Networking, RDMA, and Systems level performance.
• 3+ years of experience as an engineering team manager leading technical performance or R&D teams.
• Hands-on experience analyzing and optimizing collective communication (e.g., NCCL, MPI) and network traffic patterns for large-scale distributed AI workloads (LLM training and inference).

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