2026 Fall Applied Science Internship - Recommender Systems/ Information Retrieval (machine Learning) - United States, Phd Student Science Recruiting

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

This internship focuses on developing and evaluating new recommendation and search algorithms, building data processing pipelines, and conducting research in recommender systems and information retrieval. The role involves applying machine learning, deep learning, and NLP techniques to large-scale datasets to improve personalized experiences for Amazon customers.

What you'd actually do

  1. Design, implement, and experimentally evaluate new recommendation and search algorithms using large-scale datasets
  2. Develop scalable data processing pipelines to ingest, clean, and featurize diverse data sources for model training
  3. Conduct research into the latest advancements in recommender systems, information retrieval, and related machine learning domains
  4. Collaborate with cross-functional teams to integrate your innovative solutions into production systems, impacting millions of Amazon customers worldwide
  5. Communicate your findings through captivating presentations, technical documentation, and potential publications, sharing your knowledge with the global AI community

Skills

Required

  • PhD enrollment
  • Programming in Java, C++, Python or related language
  • Experience with 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

Nice to have

  • Publications at top-tier peer-reviewed conferences or journals
  • Experience building machine learning models or developing algorithms for business application
  • Experience with popular deep learning frameworks such as MxNet and Tensor Flow

What the JD emphasized

  • publications at top-tier peer-reviewed conferences or journals

Other signals

  • 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
  • design, implement, and experimentally evaluate new recommendation and search algorithms using large-scale datasets
  • develop scalable data processing pipelines to ingest, clean, and featurize diverse data sources for model training
  • conduct research into the latest advancements in recommender systems, information retrieval, and related machine learning domains