Principal Architect, AI Networking

About the role

The team builds systems-level software that moves data between GPUs, nodes, and storage at the speed modern AI demands—spanning low-level transport optimization, hardware-software co-design, and communication frameworks that plug directly into production AI stacks. The team's charter expands into emerging domains including quantum computing interconnects.

Responsibilities

• Setting the long-term technical vision for distributed AI communication systems—GPU-to-GPU, GPU-to-storage, and cross-node data movement.
• Conducting original research and prototyping next-generation networking solutions over RDMA, NVLink, and GPUDirect.
• Driving hardware-software co-optimization with GPU, DPU, NIC, and network switch.
• Investigating fundamental bottlenecks in communication runtimes for large-scale AI workloads (KV cache transfer, disaggregated prefill/decode, model parallelism).
• Integrating networking capabilities into AI serving stacks such as vLLM, SGLang, and TensorRT-LLM.
• Publishing findings, representing NVIDIA in industry forums and standards bodies, and mentoring senior engineers across the organization.

Requirements

• 15+ years in systems software and/or networking with deep expertise in high-performance networking (InfiniBand, RoCE, RDMA, NVLink), communication libraries (e.g. NIXL, NCCL, UCX, MPI, NVSHMEM), and GPU accelerated systems, with 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—transformer architectures, KV cache mechanics, model parallelism, or distributed training and inference patterns.
• Proficiency in programming languages such as C, C++, Rust and Python.

Qualifications

• Knowledge of ML inference frameworks (vLLM, SGLang, TensorRT-LLM) and their communication requirements.
• CUDA programming and NVIDIA GPU architecture expertise.
• Proved experience influencing product strategy and technical roadmap at a senior level.
• Major open-source contributions.

Skills

• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Ability to manage multiple projects and priorities simultaneously.

Benefits

Competitive salaries and a comprehensive benefits package, including equity and benefits.

Pay

Base salary range: 272,000 USD - 431,250 USD.

Schedule

Full-time position.

Benefits

• Health insurance
• Retirement plans
• Flexible work arrangements
• Professional development opportunities

Application Instructions

Applications for this job will be accepted at least until April 27, 2026.