Machine Learning Science Graduate - PhD San Jose, Seattle

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 we know that when one of us wins, we all win.

We provide a full benefits package, including exciting travel perks, generous time‑off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees’ passion for travel and ensure a rewarding career journey. We’re building a more open world. Join us.

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 statistical methods such as 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 and simple way to multiple partners and senior leadership (both internal and external)
• Conducting feature engineering and modifying existing models/techniques to suit business needs
• Developing domain expertise in fraud and 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
• Applying domain knowledge (e.g., travel, online retail), business acumen, and critical reasoning skills to your work

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

• Helpful to understand machine‑learning techniques such as Regression, Naïve Bayes, Gradient Boosting, Random Forests, SVMs, Neural Networks, and NLP
• Helpful to have experience with programming, statistical, and querying languages such as Python, R, SQL/Hive, Java
• Helpful to understand distributed file systems, scalable data stores, distributed computing and related technologies (Spark, Hadoop, etc.) and have implementation experience of Map Reduce 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)

Successful candidates will receive a competitive compensation package including comprehensive benefits and other perks, some of which are included below:

• The total cash range for this position in San Jose, CA is $170,000. The total cash range for this position in Seattle, WA is $160,000. Employees in this role have the potential to increase their pay based on ongoing, demonstrated, and sustained performance. Starting pay will vary based on multiple factors, including location and degree level.
• Hybrid work policy
• Travel discounts
• Medical, dental, and vision…