Senior Deep Learning Communication Architect Scalable DL

NVIDIA; Seattle (WA); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA in Seattle seeks a Deep Learning Communication Architect to scale DNN models and distributed training frameworks across massive clusters. You will optimize data transfer, design efficient protocols, and collaborate on high-speed interconnects (NVLink, InfiniBand) and libraries (MPI, NCCL, UCX).

Experience with PyTorch, TensorRT-LLM, vLLM, SGLang and strong C++/Python skills are essential for shaping next‑gen AI systems at scale.

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. CUDA and OpenCL, and familiarity with InfiniBand and RoCE networks.

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.

Skills

Deep learning

Distributed training

C++

Python

CUDA

NCCL

MPI

LLM inference

Performance optimization

Data parallelism

Education

Ph.D. in CS/EE or related

MS in CS/EE

BS in CS/EE

Tools

Torch/PyTorch

TensorRT-LLM

vLLM

SGLang

UCX/UCC

NVSHMEM

Job description

NVIDIA in Seattle seeks a Deep Learning Communication Architect to scale DNN models and distributed training frameworks across massive clusters. You will optimize data transfer, design efficient protocols, and collaborate on high-speed interconnects (NVLink, InfiniBand) and libraries (MPI, NCCL, UCX).

Experience with PyTorch, TensorRT-LLM, vLLM, SGLang and strong C++/Python skills are essential for shaping next‑gen AI systems at scale.