Deep Learning Performance Architect

NVIDIA is developing processor and system architectures that accelerate deep learning and high-performance computing applications.

We are looking for an expert deep learning system performance architect to join our AI performance modelling, analysis and optimization efforts.

In this position, you will have a chance to work on DL performance modelling, analysis, and optimization on state-of-the-art hardware architectures for various LLM workloads.

Responsibilities:

• Analyze state-of-the-art DL networks (LLM etc.), identify and prototype performance opportunities to influence SW and Architecture team for NVIDIA's current and next-gen inference products
• Develop analytical models for state-of-the-art deep learning networks and algorithms to innovate processor and system architectures design for performance and efficiency
• Specify hardware/software configurations and metrics to analyze performance, power, and accuracy in existing and future uni-processor and multiprocessor configurations
• Collaborate across the company to guide the direction of next-gen deep learning HW/SW by working with architecture, software, and product teams

Requirements:

• BS, MS or PhD in relevant discipline (CS, EE, Math, etc.) or equivalent experience
• 5+ years work experience
• Experience with popular AI models (e.g., LLM and AIGC models)
• Familiarity with typical deep learning SW frameworks (e.g., Torch/JAX/TensorFlow/TensorRT)
• Knowledge and experience on hardware architectures for deep learning applications