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NVIDIA

Designs the chips and software that power AI

Manager – Performance Research and Analysis

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • A B.Sc. or M.Sc. in Computer Science, Computer Engineering, Software Engineering, or equivalent technical experience.
  • At least 8 years of overall experience and deep expertise in high-performance networking, RDMA, and systems-level performance.
  • At least 3 years of experience as an engineering team manager leading technical performance or R&D teams.
  • Hands-on experience analyzing and optimizing collective communication, such as NCCL and MPI, and network traffic patterns for large-scale distributed AI workloads, including LLM training and inference.
  • Hands-on experience designing, deploying, and customizing Grafana dashboards for cluster monitoring, alerting, and data visualization.
  • Experience optimizing NCCL, RDMA/RoCEv2, and custom collective algorithms for multi-thousand-GPU deployments running LLMs or Mixture-of-Experts architectures is preferred.
  • Experience tuning advanced network traffic mechanisms such as adaptive routing, PFC/ECN congestion control, and packet-spraying technologies is preferred.
  • Experience building autonomous performance-driven tools, AI-assisted root-cause analysis agents, or automated regression frameworks for continuous cluster-level performance evaluation is preferred.
  • Hands-on experience developing custom Grafana plugins, complex dashboard panels, or integrated alert-management workflows using PromQL/LogQL for hyperscale or high-performance computing environments is preferred.

Responsibilities

  • Drive end-to-end performance strategy, characterization, test plans, and optimization for next-generation AI GPU clusters, focusing on large-scale distributed training and inference workloads.
  • Evaluate and optimize networking core technology performance, including RDMA/PRDMA, networking protocols, collective communication (NCCL), congestion control, and load-balancing algorithms.
  • Conduct performance research and analysis of DPUs and storage technologies in North-South use cases and deployment scenarios to maximize performance and efficiency for AI inference jobs.
  • Drive performance observability and dashboard strategy across next-generation data center solutions and supercomputers, using scalable telemetry pipelines to build dashboards and automated analytics from real-time performance metrics across NICs, switches, GPUs, and NVLink boundaries.
  • Perform root-cause analysis on complex multi-node performance bottlenecks and drive actionable mitigation plans across hardware, firmware, and software teams.

Desired Qualifications

  • Exceptional cross-team leadership, analytical thinking, and communication skills to drive alignment across hardware, software, and architecture groups.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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