Developer Technology Engineer - LLM
@ NVIDIA

Beijing, Beijing, China
$150,000
On Site
Full Time
Posted 16 hours ago

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About NVIDIA

NVIDIA has transformed computer graphics, PC gaming, and accelerated computing for over 25 years. Today, we harness AI to define the next era of computing, powering GPUs that act as the brains behind computers, robots, and self-driving cars.

What You'll Be Doing

As a Developer Technology Engineer - LLM at NVIDIA, you will:

  • Study and develop cutting-edge techniques in deep learning, graphs, machine learning, and data analytics.
  • Perform in-depth analysis and optimization for current- and next-generation GPU architectures.
  • Work on key applications like LLM training and inference to solve current and future challenges.
  • Craft and optimize core parallel algorithms and data structures through library development and direct application contribution.
  • Collaborate with diverse teams including architecture, research, libraries, tools, and system software teams.
  • Travel for on-site visits and conferences.

What We Need To See

Applicants should have:

  • An MS or PhD from a leading University in engineering or Computer Science.
  • At least 3 years of working experience.
  • Strong programming skills and a practical knowledge of AI algorithms.
  • Experience with large-scale language model (LLM) training or inference and framework development.
  • Expertise in parallel programming, ideally using CUDA C/C++.
  • Excellent communication, organization, and problem-solving skills.

Key skills/competency

LLM, deep learning, GPU, CUDA, parallel programming, optimization, data analytics, machine learning, algorithm development, AI

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How to Get Hired at NVIDIA

🎯 Tips for Getting Hired

  • Customize your resume: Highlight AI, GPU, and CUDA experience.
  • Research NVIDIA: Understand their innovation in AI and computing.
  • Focus on technical skills: Emphasize deep learning and parallel programming.
  • Prepare for interviews: Practice problem-solving and technical questions.

📝 Interview Preparation Advice

Technical Preparation

Review deep learning and CUDA fundamentals.
Practice LLM training and inference algorithms.
Study GPU architecture performance optimization.
Analyze parallel programming and data structure design.

Behavioral Questions

Describe teamwork in high-pressure projects.
Explain problem-solving during technical challenges.
Discuss conflict resolution within diverse teams.
Share time-management experiences under deadlines.

Frequently Asked Questions