Senior Software Architect, AI Systems and Networking
$224k - $431k • Remote • US, CA, Santa Clara
Posted 20h ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Infrastructure Engineer
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 facilitate high-speed data transfer between GPUs, nodes, and storage, crucial for modern AI demands. This involves optimizing low-level transport, co-designing hardware and software, and creating communication frameworks that integrate with production AI stacks. The team's scope also extends to emerging areas like quantum computing interconnects. The Senior Architect will lead modules and projects from initial research scoping through to production code delivery, acting as a recognized expert to guide technical decisions, incorporate external ideas, and prototype novel solutions.
Responsibilities
- Architect and implement high-performance communication and memory management libraries for distributed AI.
- Drive hardware-software co-optimization with GPU, DPU, NIC, and switch teams using technologies like GPUDirect RDMA, NVLink, and next-generation interconnects.
- Profile and optimize data movement across GPU memory, system DRAM, NVMe, and network fabrics.
- Integrate networking capabilities into AI serving stacks such as vLLM, SGLang, and TensorRT-LLM.
- Contribute to and maintain open-source projects, mentor engineers, conduct design reviews, and prototype experimental technologies.
Requirements
- 12+ years of experience in systems software and/or networking with a proven track record of owning complex projects.
- MS, PhD, or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
- Solid understanding of high-performance networking, including InfiniBand, RoCE, RDMA, NVLink, and GPUDirect.
- Strong systems programming skills in C/C++/Rust, with proficiency in performance profiling and low-level debugging.
- Understanding of ML systems concepts such as transformer architectures, KV cache mechanics, model parallelism, or distributed training and inference patterns.
Benefits
- Competitive salaries
- Comprehensive benefits package
- Equity
- Eligibility for benefits