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

Amazon.com Inc

Seattle, WA

JOB DETAILS
SKILLS
Algorithms, Artificial Intelligence (AI), Best Practices, Collaborative Learning, Communication Skills, Cross-Functional, Data Management, Data Modeling, Data Modeling Language, Data Processing, Data Sets, Data Structures, Deep Learning, Detail Oriented, Distributed Computing, Information Retrieval, Knowledge Engineering, Knowledge Management, Large-Scale Systems, Leading Edge Technology, Machine Learning, Natural Language Processing (NLP), Problem Solving Skills, Production Systems, Publications, Research Skills, Scalable System Development, Scripting (Scripting Languages), Search Algorithms, Team Player, Technical Presentation, Technical Writing, Unstructured Data
LOCATION
Seattle, WA
POSTED
30+ days ago

Unleash Your Potential as an AI Trailblazer

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.

As an Applied Science Intern focused on Recommender Systems and Information Retrieval in Machine Learning, you"ll have the opportunity to work alongside renowned scientists and engineers, tackling complex challenges in areas such as deep learning, natural language processing, and large-scale 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. You"ll 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, you"ll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge 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 fast-paced, 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.

Must be eligible and available for a full-time (40h / week) 12 week internship between May 2026 and September 2026

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 Learning,Neural Networks/GNNs, Large Language Models, Data Structures and Algorithms, Graph Modeling, Collaborative Filtering, Learning to Rank, Recommender Systems

In this role, you"ll 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 Amazon"s machine learning capabilities.

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.

A day in the life

  • 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

About the Company

A

Amazon.com Inc

At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.

Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.

It’s Always Day 1
At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here

Our Leadership Principles
Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles
COMPANY SIZE
10,000 employees or more
INDUSTRY
Retail
FOUNDED
1994
WEBSITE
http://Amazon.com/militaryroles