Senior Deep Learning Communication Architect

About the role

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.

Responsibilities

• Scale the DNN models and training/inference frameworks to systems with hundreds of thousands of nodes.
• Optimize communication performance: identify and eliminate bottlenecks in data transfer and synchronization during distributed deep learning training and inference.
• Design 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).
• Explore innovative communication technologies: research and evaluate new communication technologies and techniques to enhance the performance and scalability of deep learning systems.
• Develop and implement solutions: build proofs‑of‑concept, conduct experiments, and perform quantitative modeling to validate and deploy new communication strategies.

Requirements

• 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.

Skills

• Deep understanding of parallelism techniques, including Data Parallelism, Pipeline Parallelism, Tensor Parallelism, Expert Parallelism, and FSDP.
• Understanding of 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.
• Prior contributions to one or more DNN training and inference frameworks as part of previous work experience (a plus).
• Deep understanding and contributions to the scaling of LLMs on large‑scale systems (a plus).

Benefits

Competitive salaries and a generous benefits package; eligibility for equity and benefits.

Pay

The base salary range is 184,000 USD – 287,500 USD for Level 4, and 224,000 USD – 356,500 USD for Level 5.