Deep Learning Performance Intern - 2027
Requirements at a glance
Deep Learning Performance Intern - 2027 at NVIDIA is a role based in China, Shanghai. The posting mentions a doctorate. It does not mention visa sponsorship. Key skills include Machine learning, Deep learning, and Generative AI. Applications are submitted through Workday.
- Education
- Doctorate (PhD) (mentioned)
- Visa sponsorship
- Not mentioned in the posting
- Application platform
- Workday
Skills in this posting
- Machine learning
- Deep learning
- Generative AI
Extracted automatically from the job description. Confirm details in the full posting below.
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