Principal Architect, AI Networking
$272k - $431k • Remote • US, CA, Santa Clara
Posted 1d ago
About the job
An applied research team within NVIDIA's Networking Systems & Software Architecture group is tackling complex AI infrastructure challenges. The team develops systems-level software to optimize data movement between GPUs, nodes, and storage at the speed required for modern AI, focusing on low-level transport, hardware-software co-design, and communication frameworks integrated into AI stacks. Their work also extends to emerging areas like quantum computing interconnects. This Principal Architect role will define the research agenda and architectural direction for NVIDIA's large-scale AI systems communication, encompassing GPUs, DPUs, NICs, and heterogeneous storage. The position demands the ability to define project scope, conduct original research, publish findings, and translate breakthroughs into production-ready software for industry-wide deployment.
Responsibilities
- Set the long-term technical vision for distributed AI communication systems, including GPU-to-GPU, GPU-to-storage, and cross-node data movement.
- Conduct original research and prototype next-generation networking solutions using RDMA, NVLink, and GPUDirect.
- Drive hardware-software co-optimization across GPU, DPU, NIC, and network switches.
- Investigate fundamental bottlenecks in communication runtimes for large-scale AI workloads like KV cache transfer, disaggregated prefill/decode, and model parallelism.
- Integrate networking capabilities into AI serving stacks such as vLLM, SGLang, and TensorRT-LLM.
- Publish research findings, represent NVIDIA in industry forums and standards bodies, and mentor senior engineers.
Requirements
- 15+ years of experience in systems software and/or networking with deep expertise in high-performance networking (InfiniBand, RoCE, RDMA, NVLink) and communication libraries (NIXL, NCCL, UCX, MPI, NVSHMEM).
- Proven track record of defining and delivering complex, cross-team technical initiatives from research concept to production.
- MS, PhD, or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
- Deep understanding of computer architecture, memory hierarchies, DMA engines, and OS-level networking.
- Understanding of ML systems concepts, including transformer architectures, KV cache mechanics, model parallelism, or distributed training and inference patterns.
- Proficiency in programming languages such as C, C++, Rust, and Python.
Benefits
- Competitive salaries
- Comprehensive benefits package
- Equity