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NVIDIA

Designs the chips and software that power AI

Senior Deep Learning Communication Architect

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A Ph.D., Master's, or bachelor's degree in Computer Science, Electrical Engineering, Computer Science and Electrical Engineering, or a closely related field, or equivalent experience.
  • Six or more years of experience building or scaling deep neural networks, parallelizing deep neural network frameworks, or working with deep learning training and inference workloads.
  • Experience evaluating, analyzing, and optimizing large language model training and inference performance on state-of-the-art models and cutting-edge hardware.
  • Deep understanding of data, pipeline, tensor, and expert parallelism, and Fully Sharded Data Parallel (FSDP).
  • Understanding of emerging serving architectures such as disaggregated serving and inference servers such as Dynamo and Triton.
  • Proficiency developing code for one or more deep neural network training and inference frameworks, such as PyTorch, TensorRT-LLM, vLLM, or SGLang.
  • Strong programming skills in C++ and Python.
  • Familiarity with GPU computing, including CUDA and OpenCL, and with InfiniBand and RoCE networks.

Responsibilities

  • Scale deep neural network models and training/inference frameworks to systems with hundreds of thousands of nodes.
  • Identify and eliminate bottlenecks in data transfer and synchronization during distributed deep learning training and inference.
  • Develop and implement communication algorithms and protocols tailored to deep learning workloads to minimize communication overhead and latency.
  • Collaborate with hardware and software teams to build systems using high-speed interconnects such as NVLink, InfiniBand, and SPC-X, and communication libraries such as MPI, NCCL, UCX, UCC, and NVSHMEM.
  • Research and evaluate new communication technologies and techniques to improve the performance and scalability of deep learning systems.
  • Build proofs of concept, conduct experiments, and perform quantitative modeling to validate and deploy new communication strategies.

Desired Qualifications

  • Prior contributions to one or more deep neural network training and inference frameworks as part of previous work experience.
  • Deep understanding of and contributions to scaling large language models on large-scale systems.

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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