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Senior Deep Learning Communication Architect

NVIDIA· Architect
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Santa Clara, CA, Austin, TX$184k - $287.5k + equityFull-timePosted 7mo ago

About the role

NVIDIA seeks a Senior Deep Learning Communication Architect to scale DNN models and training/inference frameworks across large distributed systems, optimize communication performance, design efficient protocols, and collaborate across hardware/software teams to leverage high-speed interconnects and advanced communication libraries.

Responsibilities

  • The software architecture group at NVIDIA has openings for a Deep Learning Communication Architect. We scale the DNN models and training/inference frameworks to systems with hundreds of thousands of nodes.
  • Optimizing communication performance: Identify and eliminate bottlenecks in data transfer and synchronization during distributed deep learning training and inference.
  • Designing efficient communication protocols: Develop and implement communication algorithms and protocols tailored for deep learning workloads, minimizing communication overhead and latency.
  • Hardware and software co-craft: Collaborate with hardware and software teams to craft systems that effectively apply high-speed interconnects (e.g., NVLink, InfiniBand, SPC-X) and communication libraries (e.g., MPI, NCCL, UCX, UCC, NVSHMEM).
  • Exploring innovative communication technologies: Research and evaluate new communication technologies and techniques to enhance the performance and scalability of deep learning systems.
  • Developing and implementing solutions: Build proofs-of-concept, conduct experiments, and perform quantitative modeling to validate and deploy new communication strategies.

Qualifications

  • A Ph.D., Masters, or BS in Computer Science (CS), Electrical Engineering (EE), Computer Science and Electrical Engineering (CSEE), or a closely related field or equivalent experience.
  • 6+ years of experience in Building DNNs, Scaling of DNNs, Parallelism of DNN frameworks, or deep learning training and inference workloads.
  • Experience in evaluating, analyzing, and optimizing LLM training and inference performance of state-of-the-art models on cutting-edge hardware.
  • Deep understanding of parallelism techniques, including Data Parallelism, Pipeline Parallelism, Tensor Parallelism, Expert Parallelism, and FSDP.
  • Understanding of the emerging serving architectures like Disaggregated Serving and inference servers like Dynamo and Triton
  • Proficiency in developing code for one or more deep neural network (DNN) training and Inference frameworks, such as PyTorch, TensorRT-LLM, vLLM, SGLang.
  • Strong programming skills in C++ and Python.
  • Familiarity with GPU computing, including CUDA and OpenCL, and familiarity with InfiniBand and RoCE networks.

Skills

Required:Distributed deep learning optimizationParallelism techniquesDNN frameworksC++ and PythonGPU computing

Benefits

  • > Equity and benefits included. See posting for details.
Last updated Jul 3, 2026. Interested? Apply on the company site.

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NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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