NVIDIA

NVIDIA

Posted via Workday

Senior Deep Learning Communication Architect

Posted Aug 5, 2026

Role at a glance

Salary
$184K – $356.5K/yr
Location
2 Locations, California, United States
Work arrangement
On-site
Employment
Full-time
Experience
6+ years of experience in Building DNNs, Scaling of DNNs, Parallelism of DNN frameworks, or deep learning training and inference workloads.
Education
A Ph.D., Masters, or BS in Computer Science (CS), Electrical Engineering (EE), Computer Science and Electrical Engineering (CSEE), or a...

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Role Summary

AI-generated

The software architecture group at NVIDIA is seeking a Deep Learning Communication Architect to scale DNN models and training and inference frameworks across systems with hundreds of thousands of nodes. The role focuses on improving communication performance and scalability for distributed deep learning systems through hardware and software collaboration.

What You'll Do

  • Identify and eliminate bottlenecks in data transfer and synchronization during distributed deep learning training and inference.
  • Develop and implement communication algorithms and protocols tailored for deep learning workloads.
  • Collaborate with hardware and software teams to apply high-speed interconnects and communication libraries.
  • Research and evaluate new communication technologies and techniques.
  • Build proofs-of-concept, conduct experiments, and perform quantitative modeling to validate and deploy new communication strategies.

Generated from the employer's posting. Verify important details before applying.

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Qualifications

The role requires experience with DNN scaling, parallelism, LLM training and inference performance, DNN frameworks, C++ and Python, GPU computing, and high-speed networks.

Required

  • A Ph.D., Masters, or BS in Computer Science (CS), Electrical Engineering (EE), Computer Science and Electrical Engineering (CSEE), or a...
  • 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...
  • Deep understanding of parallelism techniques, including Data Parallelism, Pipeline Parallelism, Tensor Parallelism, Expert Parallelism,...
  • 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,...
  • Strong programming skills in C++ and Python.
  • Familiarity with GPU computing, including CUDA and OpenCL, and familiarity with InfiniBand and RoCE networks.

Preferred

  • Prior contributions to one or more DNN training and Inference frameworks as part of your previous work experience.
  • Deep understanding and contributions to the scaling of LLMs on large-scale systems.

Original job description

Content provided by the employer

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

What You'll Be Doing:

  • 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.

What We Need to See:

  • 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.

Ways to Stand Out from the Crowd:

  • Prior contributions to one or more DNN training and Inference frameworks as part of your previous work experience.

  • Deep understanding and contributions to the scaling of LLMs on large-scale systems.

With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most hard-working and talented people in the world working for us. If you're creative and passionate about developing cloud services we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May 24, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA

About the company

NVIDIA

Large Enterprise

NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.