Senior Deep Learning Communication Architect
Core
Architecting communication protocols and optimizing data transfer for distributed deep learning training and inference across hundreds of thousands of nodes.
Role type
Senior Deep Learning Communication Architect
Builds
Scalable distributed training and inference frameworks for AI models
Domain
AI Infrastructure / High-Performance Computing / Distributed Systems
Deliverable
production ML models
Required skills
Distributed deep learning training/inference, Communication protocol design, Parallelism techniques (Data, Pipeline, Tensor, Expert, FSDP), C++, Python, GPU computing (CUDA, OpenCL), High-speed interconnects (NVLink, InfiniBand, RoCE), Communication libraries (MPI, NCCL, UCX, UCC, NVSHMEM), LLM serving architectures (Dynamo, Triton)
Preferred skills
Contributions to DNN training/inference frameworks, Scaling LLMs on large-scale systems
Technologies
PyTorch, TensorRT-LLM, vLLM, SGLang, NVLink, InfiniBand, MPI, NCCL, UCX, UCC, NVSHMEM, CUDA, OpenCL
Responsibilities
Identify and eliminate bottlenecks in data transfer and synchronization, Develop and implement communication algorithms and protocols, Collaborate with hardware/software teams on high-speed interconnects, Research and evaluate new communication technologies, Build proofs-of-concept and conduct quantitative modeling
Seniority
Senior, hands-on IC