2026 Fall Applied Science Internship - Information & Knowledge Management (machine Learning) - United States, Phd Student Science Recruiting

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

This internship focuses on developing systems and frameworks for machine learning asset lifecycle management, leveraging NLP and information retrieval. The role involves research into ML operations and knowledge engineering to enhance Amazon's ML capabilities.

What you'd actually do

  1. Develop scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
  2. Design, development and evaluation of highly innovative ML models for solving complex business problems.
  3. Research and apply the latest ML techniques and best practices from both academia and industry.

Skills

Required

  • PhD enrollment
  • Java, C++, Python or related language
  • Knowledge Graphs and Extraction
  • Neural Networks/GNNs
  • Data Structures and Algorithms
  • Time Series
  • Machine Learning
  • Natural Language Processing
  • Deep Learning
  • Large Language Models
  • Graph Modeling
  • Programming/Scripting Languages

Nice to have

  • publications at top-tier peer-reviewed conferences or journals
  • building machine learning models or developing algorithms for business application
  • MxNet
  • Tensor Flow

What the JD emphasized

  • PhD
  • Knowledge Graphs and Extraction
  • Neural Networks/GNNs
  • Machine Learning
  • Natural Language Processing
  • Deep Learning
  • Large Language Models
  • Graph Modeling
  • Knowledge Graphs and Extraction
  • Programming/Scripting Languages
  • publications at top-tier peer-reviewed conferences or journals

Other signals

  • develop systems and frameworks that power Amazon's machine learning capabilities
  • leverage natural language processing and information retrieval techniques
  • develop innovative frameworks and tools that streamline the lifecycle of machine learning assets
  • conduct groundbreaking research into emerging best practices and innovations in the field of ML operations, knowledge engineering, and information management