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Salary
≈ $21k – $41k per year (Estimated)
Location
In office (Shanghai)
Seniority
Intern
Employment
Internship

Confirmed on the employer's own hiring board on Oct 12, 2026. First seen by Alion on Oct 12, 2026. NVIDIA scores A on the Alion truth index.

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NVIDIA is an American technology company founded in 1993 that invented the graphics processing unit and has become the dominant supplier of accelerated computing platforms for artificial intelligence. Its portfolio spans data centre GPUs and systems built on the Hopper and Blackwell architectures, GeForce consumer graphics, automotive and robotics platforms, high-speed networking acquired with Mellanox, and the CUDA software stack that binds the ecosystem together. Headquartered in Santa Clara, California, the company sells to cloud providers, enterprises, research institutions and gamers worldwide and is one of the most valuable listed businesses on the Nasdaq.

NVIDIA is developing processor and system architectures that accelerate deep learning on edge devices, workstations, and data center GPUs for a variety of applications including automotive, robotics, large language models and AI generative models. We are looking for an expert deep learning system performance architect to join our deep learning modelling, performance optimization, projections, and analysis effort. In this position, you will have the chance to optimize deep learning hardware and software architecture and make the significant impact in a dynamic technology focused company.

What you’ll be doing:

  • Benchmark and analyze performance of various machine learning/deep learning algorithms on different GPU/NPU architectures
  • Develop performance models and provide performance projections for deep learning workloads on different architectures
  • Identify architecture, software and system performance bottlenecks and propose optimizations
  • Explore new features and hardware capabilities on deep learning applications What we need to see: • BSc. MS or PhD in relevant discipline (CS, EE, Math, etc.,)
  • Be familiar with GPU or Accelerator-based deep learning platform and software stack
  • A strong background in computer architecture
  • Be familiar with LLM or generative AI deep learning algorithms and kernel optimizations
  • Experience in system architecture design and performance optimization
  • Familiar with machine learning and deep learning frameworks
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