Deep Learning Performance Architect

Responsibilities

• Analyze state-of-the-art deep learning networks (such as LLMs) to identify and prototype performance opportunities.
• Influence software and architecture teams for NVIDIA's current and next-generation inference products.
• Develop analytical models for deep learning networks and algorithms to innovate processor and system architecture design for performance and efficiency.
• Specify hardware/software configurations and metrics to analyze performance, power, and accuracy in uni-processor and multiprocessor configurations.
• Collaborate across architecture, software, and product teams to guide the direction of next-generation deep learning hardware and software.

Requirements

• BS, MS, or PhD in Computer Science, Electrical Engineering, Mathematics, or a related discipline.
• 5+ years of professional work experience.
• Proven experience with popular AI models, including Large Language Models (LLM) and AIGC.
• Familiarity with deep learning software frameworks such as PyTorch, JAX, TensorFlow, or TensorRT.
• Strong knowledge of hardware architectures specifically designed for deep learning applications.

About the Company

NVIDIA is developing processor and system architectures that accelerate deep learning and high-performance computing applications. We are a dynamic, technology-focused company working on the cutting edge of AI and hardware innovation.