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Job at NVIDIA

Deep Learning Performance Intern - 2027

Employer
NVIDIA
Location
China, Shanghai
Workplace
Not supplied
Employment
full-time
Published
First listed on USASI

Apply on NVIDIA’s site (external site)

This posting comes from NVIDIA’s own Workday job board. USASI checks that board twice a day and removes this page once the employer closes the posting. The description below is NVIDIA’s text as published; USASI does not edit it, does not recruit, and does not take applications. How jobs are collected.

Job description

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

Apply on NVIDIA’s site (external site)

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