NVIDIADeep Learning Performance Intern - 2027
NVIDIA is hiring a Deep Learning Performance Intern - 2027 in Shanghai, China. Level rates it ; you can apply on Level.
AI in this role
Optimize deep learning hardware and software architecture and benchmark ML/DL algorithms on GPU/NPU architectures as an intern.
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
How we rate this
Deep Learning Performance Intern - 2027 at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where deep learning was part of your work. What did you do?
- Tell me about a project where performance optimization was part of your work. What did you do?
- Tell me about a project where computer architecture was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where llm was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Deep learning, Performance Optimization, Computer Architecture, Machine learning, and LLM. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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