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Deep Learning Performance Architect

NVIDIA

Deep Learning Performance Architect

full-timePosted: Aug 19, 2026Updated: Aug 27, 2026Shanghai, China

Job Description

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. You will make your contributions to our dynamic technology focused company. What you'll be doing: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 productsDevelop analytical models for the state of the art deep learning networks and algorithm 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.What we need to see: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)Be familiar with typical deep learning SW framework (e.g., Torch/JAX/TensorFlow/TensorRT)Knowledge and experience on hardware architectures for deep learning applicationsWith competitive salaries and a generous benefits package, we are 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 and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, 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.#deeplearning

Locations

  • Shanghai, China
  • Beijing, China

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 the state of the art deep learning networks and algorithm 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.
  • 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 the state of the art deep learning networks and algorithm 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.

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