Applied Science Intern - Masters - 2027 - Austin, San Jose, Seattle

Expedia•San Jose, CA

18h•Hybrid

About The Position

As an Applied Science Intern in the Expedia Group 2027 Global Summer Internship Program, you will fully integrate into our inclusive community and gain a well-rounded experience of life at Expedia Group. Our global program offers an exciting opportunity to contribute to business-critical projects by developing your skills through tailored learning and training, providing ongoing support, and offering insightful intern-specific events to build knowledge and community. You will make an immediate impact on reimagining the way people search for travel by inventing brand-new techniques to power global travel for everyone, everywhere. From building pipelines and prototyping new ML models with A/B testing, to applying new techniques to services that run tens of thousands of requests per second, there are numerous opportunities for technical innovation at Expedia Group.

Requirements

Must be available to commit to the full program dates: May 24th – July 30th or June 21st – August 27th , 2027
Must be graduating with an Master’s degree in a technical subject (e.g. Master’s degree in a technical, or analytical-related, subject such as Computer Science with focus in areas like Artificial Intelligence, Machine Learning, Natural Language Processing, Data Mining, Data Science, Mathematics, Physics, Statistics, Operations Research, Electrical & Computer Engineering, or any Engineering degree) between December 2027 and July 2028
Must be willing to relocate to city of job location if outside commuting distance
Must have Python and/or Scala, SQL knowledge
Must have no more than 2 years' professional experience
Must be graduating from university in the same country of internship location

Nice To Haves

Helpful to have theoretical understanding various machine learning topics like Regression, Naïve Bayes, Deep Learning, Gradient Boosting, Random Forests, SVMs, Neural Networks
Helpful to have experience with programming, statistical, and querying languages like Python, R, SQL/Hive, Java
Helpful to have understanding of distributed file systems, scalable datastores, distributed computing and related technologies (Spark, Hadoop, etc.); implementation experience of MapReduce techniques, in-memory data processing, etc.
Helpful to be familiar with cloud computing, AWS specifically, in a distributed computing context
Helpful to be able to effectively communicate and engage with a variety of partners (e.g., internal, external, technical, non-technical people)
Helpful to have Java, R, C++, Hive, Hadoop, Microsoft SQL Server knowledge

Responsibilities

Design, prototype, and evaluate machine learning models under the guidance of more experienced machine learning scientists, contributing to well-scoped research and product-focused experiments.
Implement clean, maintainable model training and inference code using standard Software Engineering practices, including basic unit testing, code reviews, and reproducible experimentation workflows.
Participate in low-level system design, API design, and data modeling for ML-powered features, learning how to translate product and data requirements into robust, production-aware ML solutions.
Explore, analyze, and prepare data using fundamental statistical and ML techniques to improve model quality, robustness, and fairness, including feature engineering and basic error analysis.
Collaborate with peers, mentors, data scientists, engineers, and product partners to understand problem spaces, define success metrics, and iterate on ML solutions that deliver measurable impact.
Learn to work with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including safely integrating and operating AI/ML-enabled solutions that improve outcomes within defined guidance and scope.