Senior Software Architect, AI Networking

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by excellent technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Join our team and discover how you can build a lasting impact on the world.

NVIDIA is seeking a sharp, innovative, and hands-on Architect to help shape the future of LLM inference at scale. Join our dynamic E2E Architecture group, where we build ground breaking systems powering the next generation of generative AI workloads. In this role, you will work across software and hardware domains to design and optimize inference infrastructure for large language models running on some of the most sophisticated GPU clusters in the world. You’ll help define how AI models are deployed and scaled in production, driving decisions on everything from memory orchestration and compute scheduling to inter-node communication and system-level optimizations. This is an opportunity to work with top engineers, researchers, and partners across NVIDIA and leave a mark on the way generative AI reaches real-world applications.

What You’ll Be Doing

• Design and evolve scalable architectures for multi-node LLM inference across GPU clusters.
• Develop infrastructure to optimize latency, throughput, and cost-efficiency of serving large models in production.
• Collaborate with model, systems, compiler, and networking teams to ensure complete, high-performance solutions.
• Prototype novel approaches to KV cache handling, tensor/pipeline parallel execution, and dynamic batching.
• Evaluate and integrate new software and hardware technologies relevant to Core Spectrum-X technologies, such as load balancing, telemetry, congestion control, vertical application integration.
• Work closely with internal teams and external partners to translate high-level architecture into reliable, high-performance systems.
• Author design documents, internal specs, and technical blog posts and contribute to open-source efforts when appropriate.
  
  

• Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or equivalent experience.
• 8+ years of experience building large-scale distributed systems or performance-critical software.
• Deep understanding of deep learning systems, GPU acceleration, and AI model execution flows and/or high performance networking.
• Proven software engineering skills in C++ and/or Python, preferably demonstrate strong familiarity with CUDA or similar platforms.
• Strong system-level thinking across memory, networking, scheduling, and compute orchestration.
• Excellent communication skills and ability to collaborate across diverse technical domains.
  
  

• Experience working on LLM - training or inference pipelines, transformer model optimization, or model-parallel deployments.
• Proven success in profiling and optimizing performance bottlenecks across the LLM training or inference stack.
• AI Accelerators and distributed communication patterns, congestion control and/or load balancing.
• Shown optimization process for complex systems, deployed at scale to make impact.
• Proven experience successfully driving complex organizational processes from planning through implementation.