System Software Architect, AI and GPU Networking

System Software Architect, AI and GPU Networking at NVIDIA

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, and they are hands-on, producing both detailed technical work and high-level architectural designs.

As an architect in the AI Networking Research team, you will explore technological challenges in accelerating networking and building AI data centers. This includes developing and researching new transport functions and semantics for optimizing AI workloads, AI systems communication, and accelerations. You will also contribute to 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, and storage.

Key Responsibilities

• 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.
• Design and prototype features and optimizations that accelerate data movement and enable new capabilities for inference and model serving, focusing on throughput, latency, and memory efficiency.
• Identify and evaluate new technologies, innovations, and partner relationships for alignment with our technology roadmap and business value.
• Develop and evaluate innovative features with respect to runtime systems, communication libraries, and AI-specific technologies.
• Develop and evaluate enhancements to communication libraries such as NIXL, UCX, and GPUnetIO, tailored to the unique demands of AI workloads.

What We're Looking For

• Hold a 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 background in C++, Python, and ideally CUDA or other GPU programming models.
• Ability and flexibility to work and communicate effectively in a multi-national, multi-time-zone corporate environment.

Bonus Points

• 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, FDSP, and others.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Key Skills/Competency

• AI Networking
• GPU Acceleration
• System Architecture
• Performance Optimization
• C++ Programming
• Python Programming
• CUDA
• Distributed AI
• Deep Learning Solutions
• Data Center Technologies