Senior System Software Architect, AI and GPU Networking

NVIDIA has been defining computer graphics, PC gaming, and accelerated computing for more than 25 years. With an outstanding legacy of innovation, driven by phenomenal technology, and extraordinary people, NVIDIA is looking for a strong technical senior architect to join us in shaping the future. Senior Architects are innovators who can translate business needs into workable technology solutions. Their expertise is deep and broad. They are hands on, producing both detailed technical work and high-level architectural designs. As a Senior architect in the AI Networking Research team, you will explore technological challenges on accelerated networking and building AI data centers. Research new transport functions and semantics for optimizing AI workloads, AI systems communication and accelerations and much more. You will also be leading architectural and development efforts across numerous technological fields, related to the modern AI data center, such as distributed AI and deep learning solutions, data analytics, High Performance Computing (HPC), Software Defined Networking (SDN), virtualization, storage, and more. What you’ll be doing: 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. Co-design hardware features (e.g., in GPUs, DPUs, or interconnects) 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 such as runtime systems, communication libraries, AI-specific technologies. Lead proof-of-concept development to evaluate and drive such technologies. What we need to see: 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 A I 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. Ways to stand out from the crowd: Shown research track record. Have 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, like Prefill/Decode, data parallelism, Tensor parallelism, FSDP and others.