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# Senior Software Architect, AI Systems and Networking

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $224,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon S3, Profiling, Computer Engineering, Extract Transform Load (ETL), Software Debugging, Distributed Computing Environment, Dynamic Random-Access Memory, Memory Management, InfiniBand, Storage Area Network (SAN), Remote Direct Memory Access, Software Engineering, System Programming, Systems Integration, Reinforcement Learning, Large Language Models, Information Technology, Transformer Architectures, SGLang, Machine Learning Operations, TensorRT, VLLM, Nvme
- **Published:** October 31, 2026
- **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Software-Architect--AI-Networking_JR2008501

## About the Role

* 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.

Ways to stand out from the crowd:

 * 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.

## Description

* 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