Zoom-posted 22 days ago
Full-time • Mid Level
Hybrid • Seattle, WA
5,001-10,000 employees

We are seeking an experienced AI Infrastructure Engineer to join our AI Incubation team. You will build and optimize large-scale training infrastructure for Large Language Models (LLMs). The ideal candidate will combine engineering fundamentals with practical experience in AI infrastructure development, demonstrating both technical depth and the ability to deliver scalable solutions for complex AI systems. About the Team With eight specialized departments, the engineering team functions as a highly collaborative, diverse powerhouse. Each department mission is to deliver seamless and innovative communication solutions. These range from software development and machine learning to quality assurance teams that work to create and maintain Zoom's user-friendly interfaces and robust infrastructure. The team continues to push the boundaries of communication technology, bringing people together regardless of their physical distance.

  • Designing and developing scalable AI infrastructure solutions for training and deploying large language models
  • Building and optimizing distributed training platforms using cutting-edge technologies
  • Implementing and maintaining containerized AI environments using Docker and Kubernetes
  • Optimizing CUDA kernels for maximum GPU utilization and performance
  • Developing platform software to support AI/ML workflows
  • Collaborating with AI researchers to implement efficient training and inference pipelines
  • Possess a Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Cognitive Science, or a related field.
  • Have 5+ years of software engineering experience with a focus on infrastructure and systems
  • Exhibit expertise in GPU programming and CUDA optimization
  • Have experience with container technologies (Docker, Kubernetes), distributed systems and cloud computing
  • Have experience building large-scale distributed systems and optimizing neural network performance
  • Demonstrate programming skills in Python, C++, and CUDA, with deep learning frameworks (PyTorch, Transformers)
  • Have a deep understanding of neural network architectures and training methodologies
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