Machine Learning Science Graduate - PhD - 2026 - San Jose, Seattle

Expedia Group•Seattle, WA

2d•Hybrid

About The Position

Expedia Group brands power global travel for everyone, everywhere. We design cutting-edge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success. Why Join Us? To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win. Expedia Group brands power global travel for everyone, everywhere. We design cutting-edge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success. Why Join Us? To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win. In this role you will: Travel is so much more than simply reaching your destination. Along the way you will make an immediate impact on reimagining the way people search for travel with our awesome team 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 is no shortage of opportunities for technical innovation at Expedia Group – the sky’s the limit! Applying statistics methods like confidence intervals, point estimates and sample size estimates to make sound and confident inferences on data and A/B tests Applying Natural Language models to Google keyword analysis and applying meta models to our multi-objective ranking problem Communicating complex analytical topics in a clean & simple way to multiple partners and senior leadership (both internal & external) Conducting feature engineering and modifying existing models/techniques to suit business needs Developing domain expertise in fraud & risk to understand how to detect risky transactions Modeling rich and complex online travel data to understand, predict and optimize business metrics to help improve the traveler experience Framing business problems as data science problems with a concrete set of tasks Apply your domain (i.e. travel, online retail) knowledge, business acumen (understanding the underlying business objectives), and critical reasoning skills to your work

Requirements

Must be available to start on August 17, 2026
Must be graduating between December 2025 and July 2026 with a PhD 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
Must be willing to relocate to city of job location if outside commuting distance

Nice To Haves

Helpful to understand ML techniques like Regression, Naïve Bayes, Gradient Boosting, Random Forests, SVMs, Neural Networks, and NLP
Helpful to have experience with programming, statistical, and querying languages like Python, R, SQL/Hive, Java
Helpful to understand 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)

Responsibilities

Applying statistics methods like confidence intervals, point estimates and sample size estimates to make sound and confident inferences on data and A/B tests
Applying Natural Language models to Google keyword analysis and applying meta models to our multi-objective ranking problem
Communicating complex analytical topics in a clean & simple way to multiple partners and senior leadership (both internal & external)
Conducting feature engineering and modifying existing models/techniques to suit business needs
Developing domain expertise in fraud & risk to understand how to detect risky transactions
Modeling rich and complex online travel data to understand, predict and optimize business metrics to help improve the traveler experience
Framing business problems as data science problems with a concrete set of tasks
Apply your domain (i.e. travel, online retail) knowledge, business acumen (understanding the underlying business objectives), and critical reasoning skills to your work