2025 Applied Science Internship - Recommender Systems Information Retrieval Machine Learning - United States PhD Student Science Recruiting

Unleash Your Potential as an AI Trailblazer

At Amazon were 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. Were seeking brilliant minds to join us as interns and contribute to the development of cuttingedge AI solutions that will shape the future of personalized experiences.

As an Applied Science Intern focused on Recommender Systems and Information Retrieval in Machine Learning youll have the opportunity to work alongside renowned scientists and engineers tackling complex challenges in areas such as deep learning natural language processing and largescale distributed systems. Your contributions will directly impact the products and services used by millions of Amazon customers worldwide.

Imagine a role where you immerse yourself in groundbreaking research exploring novel machine learning models for product recommendations personalized search and information retrieval tasks. Youll leverage natural language processing and information retrieval techniques to unlock insights from vast repositories of unstructured data fueling the next generation of AI applications.

Throughout your journey youll have access to unparalleled resources including stateoftheart computing infrastructure cuttingedge research papers and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically communicate effectively and thrive in a fastpaced innovative environment where bold ideas are celebrated.

Join us at the forefront of applied science where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn grow and leave an indelible mark on the world of technology.

Amazon has positions available for Machine Learning Applied Science Internships in but not limited to Arlington VA; Bellevue WA; Boston MA; New York NY; Palo Alto CA; San Diego CA; Santa Clara CA; Seattle WA.

Key job responsibilities
We are particularly interested in candidates with expertise in: Knowledge Graphs and Extraction Programming/Scripting Languages Time Series Machine Learning Natural Language Processing Deep LearningNeural Networks/GNNs Large Language Models Data Structures and Algorithms Graph Modeling Collaborative Filtering Learning to Rank Recommender Systems

In this role youll collaborate with brilliant minds to develop innovative frameworks and tools that streamline the lifecycle of machine learning assets from data to deployed models in areas at the intersection of Knowledge Management within Machine Learning. You will conduct groundbreaking research into emerging best practices and innovations in the field of ML operations knowledge engineering and information management proposing novel approaches that could further enhance Amazons machine learning capabilities.

The ideal candidate should possess the ability to work collaboratively with diverse groups and crossfunctional teams to solve complex business problems. A successful candidate will be a selfstarter comfortable with ambiguity with strong attention to detail and the ability to thrive in a fastpaced everchanging environment.

A day in the life
Design implement and experimentally evaluate new recommendation and search algorithms using largescale 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 crossfunctional 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

Are enrolled in a PhD
Are 18 years of age or older
Work 40 hours/week minimum and commit to 12 week internship maximum
Can relocate to where the internship is based
Experience programming in Java C Python or related language
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

Have publications at toptier peerreviewed 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

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $65.38/hr in our lowest geographic market up to $107.40/hr in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on jobrelated knowledge skills and experience. Amazon is a total compensation company. Dependent on the position offered equity signon payments and other forms of compensation may be provided as part of a total compensation package in addition to a full range of medical financial and/or other benefits. For more information please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.