Applied Science Intern - ML Research (Hybrid)

Expedia Group; Austin (TX); On-site; USD 77,000 - 84,000; Full time

Benefits offered by this job

Health care benefits

Hybrid Work Policy

Travel discounts

Relocation support

Job summary

Expedia Group is offering a Summer Internship Program as an Applied Science Intern in Austin. You will join one of our teams to develop ML models, participate in hands-on projects, and learn through tailored training sessions and mentorship.

Successful candidates will work on real business priorities, collaborate with data scientists and engineers, and gain exposure to AI-driven systems with safe integration into production workflows.

Qualifications

• Must be available to commit to the full program dates: May 24th – July 30th; June 21st – August 27th.
• Must be graduating with a PhD degree in a technical subject 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.
• Helpful to have knowledge of distributed computing and related technologies (Spark, Hadoop).
• Helpful to have understanding of cloud computing (AWS).

Responsibilities

• Design, prototype, and evaluate machine learning models under guidance of senior scientists.
• Implement clean, maintainable model training and inference code with unit tests and reproducible experiments.
• Participate in low-level system design, API design, and data modeling for ML-powered features.
• Explore and analyze data using statistical and ML techniques to improve model quality.
• Collaborate with peers and partners to define success metrics and iterate on ML solutions.

Skills

Python

Scala

SQL

Java

Education

PhD degree in a technical subject

Tools

Spark

Hadoop

Hive

AWS

Job description

Expedia Group is offering a Summer Internship Program as an Applied Science Intern in Austin. You will join one of our teams to develop ML models, participate in hands-on projects, and learn through tailored training sessions and mentorship.

Successful candidates will work on real business priorities, collaborate with data scientists and engineers, and gain exposure to AI-driven systems with safe integration into production workflows.