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

Amazon
Arlington, VA / Bellevue, WA / Boston, MA2026-04-14ONSITE

About the job

At Amazon, we're on a mission to revolutionize the way people discover and access information. Our Applied Science team is at the forefront of this endeavor, pushing the boundaries of recommender systems and information retrieval. We're seeking brilliant minds to join us as interns and contribute to the development of cutting-edge AI solutions that will shape the future of personalized experiences.

Responsibilities

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

Collaborate with cross-functional teams to integrate your innovative solutions into production systems, impacting millions of Amazon customers worldwide

Communicate your findings through captivating presentations, technical documentation, and potential publications, sharing your knowledge with the global AI community

Qualifications

Minimum

Are enrolled in a PhD

Can relocate to where the internship is based

Experience programming in Java, C++, Python or related language

Work 40 hours/week minimum and commit to 12 week internship minimum

Experience with one or more of the following: 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, Knowledge Graphs and Extraction, Programming/Scripting Languages

Preferred

The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment.