Senior Hpc and AI Networking Performance Research and Analysis Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Research and analysis engineer focused on optimizing AI networking performance for large-scale LLM training on distributed GPU clusters, involving profiling, analysis, tool development, and collaboration across hardware and software teams.

What you'd actually do

  1. Exploring and researching AI workloads and DL models specifically tailored for large-scale deep learning LLM training on NVIDIA supercomputers and distributed systems focusing on high-performance networking and Nvidia Collective Communications Library (NCCL).
  2. Benchmarking, Profiling, and Analyzing the performance to find bottlenecks and identify areas of improvement and optimizations, with a strong emphasis on networking aspects.
  3. Implementing performance analysis tools.
  4. Collaborating with many teams from hardware to software to provide performance analysis insights.
  5. Defining performance test planning , setting performance expectations for new technologies and solutions, and working to reach the performance targets limits.

Skills

Required

  • high-performance Networking (RDMA, MPI, NCCL, Congestion Control Algorithms)
  • Performance Analysis skills and methodologies
  • NVIDIA GPUs
  • CUDA library
  • deep learning frameworks like TensorFlow or PyTorch
  • networking collective communication libraries (such as NCCL)
  • protocols (such as RoCE and RDMA)
  • Python
  • Bash
  • C languages
  • Linux OS distros

Nice to have

  • In-depth knowledge and experience with AI workloads and benchmarking for distributed LLM training
  • Knowledge in CUDA, and NCCL libraries
  • Knowledge in Congestion Control algorithms
  • In-depth System knowledge and understanding (Intel / AMD / ARM CPUs, NVIDIA GPUs, HCA, Memory, PCI)
  • Strong Performance Analysis skills and methodologies using modern tools

What the JD emphasized

  • 5+ years of experience with high-performance Networking (RDMA, MPI, NCCL, Congestion Control Algorithms)
  • Demonstrated Performance Analysis skills and methodologies.
  • Experience with NVIDIA GPUs, CUDA library, deep learning frameworks like TensorFlow or PyTorch, combined with expertise in networking collective communication libraries (such as NCCL) and protocols (such as RoCE and RDMA).

Other signals

  • large-scale deep learning LLM training
  • distributed systems
  • high-performance networking
  • performance analysis
  • bottlenecks
  • optimizations