Senior Architect

NVIDIA is looking for a strong technical senior architect to join us in shaping the future. As a Senior Architect in the AI Networking Research team, you will explore technological challenges on accelerated networking and building AI data centers.

You will enhance NVIDIA's GPU Networking offerings for accelerating AI workloads, such as NVIDIA Dynamo, NVIDIA NIXL, and NVIDIA UCX, tailored to the unique requirements of AI workloads. You will co-design hardware features that accelerate data movement and enable new capabilities for inference and model serving.

Responsibilities:

• Enhance NVIDIA's GPU Networking offerings for accelerating AI workloads
• Co-design hardware features that accelerate data movement and enable new capabilities for inference and model serving
• Identify and evaluate new technologies, innovations, and partner relationships for alignment with our technology roadmap and business value
• Lead architecture and design of new technologies and innovations
• Lead proof-of-concept development to evaluate and drive such technologies

Requirements:

• Hold a B.Sc., M.Sc., or Ph.D. in Computer Science, Electrical or Computer Engineering from a leading university (or equivalent experience)
• 5+ years of industry experience (or equivalent) in system architecture, AI systems architecture, scaling of AI, parallelism of AI frameworks, or deep learning training workloads
• Experienced in algorithm design, system programming, computer architecture, and operating systems
• Experienced in virtualization, networking, and storage
• Deep understanding of performance profiling and optimization techniques, together with defining and using hardware features
• Strong programming and software development skills
• Ability and flexibility to work and communicate effectively in a multi-national, multi-time-zone corporate environment

Preferred qualifications:

• Shown research track record
• Experience and passion for system architecture, CPU/GPU/memory/storage/networking
• Stellar communication skills
• Knowledge in Deep Learning frameworks and AI communication libraries (NCCL, UCX, MPI, and equivalents)
• Deep understanding of Inference and Training workloads and optimizations