NVIDIA
Senior Deep Learning Communication Architect
$184K–$356K
US, CA, Santa Clara +1 · Senior · Full-time · Posted May 20, 2026
- Location
- US, CA, Santa Clara
US, TX, Austin - Experience
- 6+ years in building or scaling DNNs, parallelism of DNN frameworks, or deep learning training and inference workloads.
- Education
- 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.
About the job
Designs and optimizes communication architectures, protocols, and software for scaling deep learning training and inference across large distributed systems.
What you'll do
- Identify and remove data-transfer and synchronization bottlenecks in distributed deep learning workloads.
- Develop communication algorithms and protocols, and collaborate across hardware and software teams on systems using high-speed interconnects and communication libraries.
- Research, prototype, model, and test new communication strategies for performance and scalability.
What they ask for
- Degree in CS, EE, CSEE, or a closely related field, or equivalent experience.
- Experience evaluating, analyzing, and optimizing LLM training and inference performance on state-of-the-art models and hardware.
- Strong programming skills in C++ and Python.
- Familiarity with GPU computing, CUDA and OpenCL, and InfiniBand and RoCE networks.
Nice to have
- Prior contributions to DNN training or inference frameworks.
- Deep understanding of and contributions to scaling LLMs on large-scale systems.
Pay
184,000 USD - 287,500 USD base salary (Level 4); 224,000 USD - 356,500 USD base salary (Level 5)
Tools
NVLink, InfiniBand, SPC-X, MPI, NCCL, UCX, UCC, NVSHMEM
AI focus
Optimizes communication for LLM and other deep learning training and inference; NVIDIA also uses AI tools in its recruiting processes.
Model-written summary · the original posting is authoritative