2026 Fall Applied Science Internship - Recommender Systems/ Information Retrieval (Machine Learning) - United States, PhD Student Science Recruiting
Core
PhD student intern developing cutting-edge AI solutions for recommender systems and information retrieval to shape personalized experiences for millions of Amazon customers.
Role type
PhD student Applied Science Intern (Recommender Systems/Information Retrieval)
Builds
Production ML models for product recommendations, personalized search, and information retrieval tasks.
Domain
E-commerce, Machine Learning, Information Retrieval
Deliverable
production ML models
Required skills
Knowledge Graphs and Extraction, Neural Networks/GNNs, Large Language Models, Time Series, Deep Learning, Natural Language Processing, Data Structures and Algorithms, Graph Modeling, Collaborative Filtering, Learning to Rank, Recommender Systems
Preferred skills
Publications at top-tier peer-reviewed conferences, building ML models for business application, experience with MxNet and Tensor Flow
Technologies
Java, C++, Python, MxNet, Tensor Flow
Responsibilities
Design and evaluate new recommendation and search algorithms using large-scale datasets; Develop scalable data processing pipelines for model training; Conduct research into advancements in recommender systems and information retrieval; Collaborate with cross-functional teams to integrate solutions into production systems; Communicate findings through presentations, documentation, and publications.
Seniority
PhD Student, Research & Development