Senior Software Architect, AI Systems and Networking

An applied research team within NVIDIA’s Networking Systems & Software Architecture group is solving some of AI’s hardest infrastructure problems. 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.

About the role

The Senior Architect role is to own modules and projects end-to-end—from scoping research questions to shipping production code. It calls for a recognized expert who drives technical decisions, pulls in ideas from research and industry, and regularly prototypes new approaches to prove a point. The work lives at the boundary of applied research and production engineering.

Responsibilities

• Architecting and implementing high-performance communication and memory management libraries for distributed AI
• Driving hardware-software co-optimization with GPU, DPU, NIC, and switch teams through GPUDirect RDMA, NVLink, and next-generation interconnects
• Profiling and optimizing data movement across GPU memory, system DRAM, NVMe, and network fabrics
• Integrating networking capabilities into AI serving stacks such as vLLM, SGLang, and TensorRT-LLM
• Contributing to and maintaining open-source projects, mentoring engineers, conducting design reviews, and prototyping experimental technologies to evaluate their viability

Requirements

• 12+ years in systems software and/or networking with demonstrated ownership of complex projects
• MS, PhD or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field
• Solid understanding of high-performance networking: InfiniBand, RoCE, RDMA, NVLink, GPUDirect
• Strong C/C++/Rust systems programming with comfort in performance profiling and low-level debugging
• Understanding of ML systems concepts—transformer architectures, KV cache mechanics, model parallelism, or distributed training and inference patterns

Skills

• Knowledge of ML inference frameworks (vLLM, SGLang, TensorRT-LLM) and their communication requirements
• Knowledge of storage networking (NVMe-oF, GPUDirect Storage, S3)
• Background of Reinforcement Learning systems

Pay

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6. You will also be eligible for equity and benefits.

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