Skip to content

NVIDIA

Manager, Performance Research and Analysis

NVIDIA is hiring a Manager, Performance Research and Analysis. Level rates it ; you can apply on NVIDIA.

Level's read on this job

  • Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
  • The posting mentions Nccl, Mpi and Rdma among its tools and skills.
  • Level classes it as a lead role.
  • NVIDIA has 2,700+ other open roles on Level.

AI in this role

Lead performance research and optimization for large-scale distributed AI GPU training and inference clusters.

NcclMpiRdmaNvlink
performance-optimizationdistributed-trainingcluster-managementnetworking

NVIDIA is seeking a highly skilled and versatile Performance Research and Analysis Manager to join our Performance Group. This role will drive end-to-end performance strategy and execution for next-generation NVIDIA data centers and solutions based on GPU systems, NIC, Switch, DPU and Networking technologies. The ideal candidate will oversee, evaluating, and optimizing end-to-end AI GPU cluster-level performance for scaling out large scale distributed training and inference jobs communication. The role will focus heavily on RDMA, Networking Protocols, Collective Communication, Congestion Control, and Load Balancing algorithms. Secondarily, you will lead NVIDIA DPUs and Storage technologies for N-S use cases to support AI Inference jobs. Third, you will drive our Performance Dashboards and Observability for cluster-level performance analysis from a stream line telemetry across NICs, Switches, GPUs, and NVlink.

What you'll be doing:

  • 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 NVLink boundaries.

  • 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).

  • Hands-on experience designing, deploying, and customizing Grafana dashboards for cluster monitoring, alerting, and data visualization.

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

Ways to stand out from the crowd:

  • Proven track record of optimizing NCCL, RDMA/RoCEv2, and custom collective algorithms specifically tailored for multi-thousand GPU deployments running LLMs or Mixture-of-Experts (MoE) architectures.

  • Deep experience tuning advanced network traffic mechanisms such as adaptive routing, PFC/ECN congestion control, and packet-spraying technologies.

  • Experience building autonomous performance-driven tools, AI-assisted root cause analysis agents, or automated regression frameworks for continuous cluster-level performance evaluation.

  • Hands-on experience developing custom Grafana plugins, complex dashboard panels, or integrated alert management workflows using PromQL/LogQL for hyperscale or HPC environments.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

#LI-Hybrid

How we rate this

Manager, Performance Research and Analysis at NVIDIA rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Performance OptimizationDistributed TrainingCluster ManagementNetworkingNcclMpiRdmaNvlink

Questions you could be asked

  1. Tell me about a project where performance optimization was part of your work. What did you do?
  2. Tell me about a project where distributed training was part of your work. What did you do?
  3. Tell me about a project where cluster management was part of your work. What did you do?
  4. Tell me about a project where networking was part of your work. What did you do?
  5. Walk me through how you've used Nccl in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Performance Optimization, Distributed Training, Cluster Management, Networking, and Nccl. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

Get your CV reviewed before you apply

Send your CV and the role you want next. Your review arrives by email within 3 days.

Get new research jobs (Works on AI ●●●○ or higher) by email

One email a week, each job rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.

Free.

Similar roles

Research roles that work on AI, at other companies.

What kind of AI work fits you?

Answer 12 practical questions in about three minutes. Get a simple profile, the work it points to, and live roles to explore next.

Find my next step

More jobs at NVIDIA

Related searches

Same AI level