Senior Deep Learning Communication Architect

NVIDIA; Austin (TX); On-site; USD 184,000 - 287,500; Full time

Benefits offered by this job

Generous benefits package

Job summary

A leading technology company in Austin, Texas, seeks a Deep Learning Communication Architect to optimize data transfer and synchronization in distributed deep learning. This role requires deep knowledge of DNN frameworks, programming expertise in C++ and Python, and at least 6 years of experience in deep learning. The company offers competitive salaries ranging from $184,000 to $356,500, along with generous benefits. Applications are accepted until March 16, 2026.

Qualifications

• 6+ years of experience building and scaling deep neural networks (DNNs).
• Experience optimizing LLM training and inference performance.
• Strong understanding of parallelism techniques such as Data, Pipeline, and Tensor Parallelism.

Responsibilities

• Optimize communication performance in distributed deep learning training.
• Design and implement efficient communication protocols.
• Collaborate with hardware and software teams to enhance system performance.

Skills

Deep learning frameworks (PyTorch, TensorRT‑LLM, vLLM, SGLang)

Programming skills in C++

Programming skills in Python

GPU computing (CUDA, OpenCL)

Education

Ph.D., Master’s, or BS in Computer Science, Electrical Engineering, or related field

Tools

NVLink

InfiniBand

MPI

NCCL

UCX

UCC

NVSHMEM

Job description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing, where the GPU acts as the brains of computers, robots, and self-driving cars that can understand the world.

What You'll Be Doing

• The software architecture group 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., Master’s, or BS in Computer Science, Electrical Engineering, Computer Science and Electrical Engineering, or a closely related field, or equivalent experience.
• 6+ years of experience in building DNNs, scaling DNNs, parallelizing DNN frameworks, or deep learning training and inference workloads.
• Experience 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 emerging serving architectures (e.g., Disaggregated Serving) and inference servers such as Dynamo and Triton.
• Proficiency developing code for one or more deep neural network frameworks, such as PyTorch, TensorRT‑LLM, vLLM, SGLang.
• Strong programming skills in C++ and Python.
• Familiarity with GPU computing (CUDA, OpenCL) and InfiniBand/ 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.

Competitive salaries and a generous benefits package are offered. Base salary ranges from $184,000 to $287,500 for Level 4 and $224,000 to $356,500 for Level 5, based on location, experience, and comparable roles. Equity and additional benefits are also available.

Applications for this job will be accepted until March 16, 2026.

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NVIDIA is committed to fostering a diverse work environment and proudly is an equal opportunity employer. We do not discriminate 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.