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Senior Performance Research and Analysis Engineer

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NVIDIA

2mo ago

Applications are closed

  • Job
    Full-time
    Senior Level
  • Software Engineering

Requirements

  • What we need to see:
  • B.Sc in Computer Science or Software Engineering
  • 5+ years of experience with high-performance Networking (RDMA, MPI)
  • 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).
  • Fast and self-learning capabilities with strong analytical and problem-solving skills.
  • Programming Languages: Python, Bash and C languages
  • Experience with Linux OS distros.
  • Team player with good communication and interpersonal skills
  • Ways to stand out from the crowd:
  • 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.

Responsibilities

  • Experience and research AI workloads and DL models specifically tailored for large-scale deep learning LLM training on NVIDIA supercomputers with a focus on High-performance networking.
  • Benchmarking, Profiling, and Analyzing the performance to find bottlenecks and identify areas of improvement and optimizations, with a strong emphasis on networking aspects.
  • Implement performance analysis tools.
  • Collaborating with many teams from HW to SW to provide performance analysis insights.
  • Define performance test planning , set performance expectations for new technologies and solutions, and work to reach the performance targets limits.

FAQs

What will be the main focus of this role as a Senior Performance Research and Analysis Engineer at NVIDIA?

The main focus of this role will be to profile and analyze AI workloads on large GPUs and CPUs scale clusters for distributed Deep Learning LLM training, specifically focusing on collectives communication and networking.

What types of hardware and platforms will I be working with in this role?

In this role, you will work and interact with various types of hardware and platforms such as HCAs, Switches, CPUs, GPUs, and Systems.

What tools and methodologies will I be using to perform performance analysis in this role?

You will have the opportunity to develop and use performance analysis tools and methodologies to deeply analyze performance expectations, limitations, and bottlenecks in AI workloads on large GPU and CPU scale clusters.

NVIDIA

Manufacturing & Electronics
Industry
10,001+
Employees
1993
Founded Year

Mission & Purpose

Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling the creation of the metaverse. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.