Skip to content
Updated How AI-exposed are the skills employers are hiring for? See the data →

From the employer's careers site First seen 18 May 2026 · Still live on 30 Oct 2026

Senior Software Architect, AI Systems and Networking

NVIDIA Santa Clara On-site senior full-time

The employer hasn't listed a salary.

Apply on NVIDIA

Description

We are seeking a Senior Software Architect to join our AI Systems and Networking team. As a Senior Software Architect, you will be responsible for owning modules and projects end-to-end, from scoping research questions to shipping production code. This role requires a recognized expert who drives technical decisions, pulls in ideas from research and industry, and regularly prototypes new approaches to prove a point.

Key responsibilities include:

  • 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

To be successful in this role, you will need to have 8+ years of experience in systems software and/or networking with demonstrated ownership of complex projects. You should also have a solid understanding of high-performance networking, including InfiniBand, RoCE, RDMA, NVLink, and GPUDirect. Additionally, you should be proficient in C/C++/Rust systems programming and have a strong understanding of ML systems concepts.

If you are a motivated and experienced software architect looking to join a dynamic team, please apply for this exciting opportunity.

Skills

Required

  • C/C++/Rust systems programming
  • High-performance networking
  • ML systems concepts
  • GPU, DPU, NIC, and switch teams
  • GPUDirect RDMA, NVLink, and next-generation interconnects

Nice to have

  • Knowledge of ML inference frameworks (vLLM, SGLang, TensorRT-LLM)
  • Knowledge of storage networking (NVMe-oF, GPUDirect Storage, S3)
  • Background of Reinforcement Learning systems
Apply on NVIDIA