Machine Learning Engineering Intern (phd)

Airbnb Airbnb · Consumer · United States · Intern

Airbnb is seeking a Machine Learning Engineering Intern (PhD) for their 2026 Summer Intern Program. The intern will work on applying new advancements in ML and AI to search relevance and personalization, driving a project from end-to-end. The role involves collaboration, communication with stakeholders, and contributing to challenging projects within the Search Relevance and Personalization team.

What you'd actually do

  1. Own and drive a capstone project from beginning to end
  2. Collaborate with multiple team members to achieve project milestones
  3. Communicate with stakeholders across different teams to provide project updates
  4. Proactively seek and provide feedback throughout the internship
  5. Actively participate in and contribute to the Engineering org and broader Airbnb community

Skills

Required

  • Studying Computer Science or a related field
  • Knowledge of Computer Science fundamentals including data structures, algorithms, design patterns, etc.
  • Proficient in one or more of the following languages and tools: Python, Pytorch, C++, SQL, Spark, Scala
  • Work authorization for employment in the United States is required (CPT/OPT is accepted)

Nice to have

  • Publications in top Machine Learning, Computer Vision or NLP conferences are a big plus.
  • Professional (full-time) experience as a software engineer/developer or intern is a plus.
  • Leadership position, community involvement, and or teaching/mentoring experience is a plus!

What the JD emphasized

  • PhD students
  • expected graduation between December 2025 - January 2026
  • Work authorization for employment in the United States is required

Other signals

  • applying new advancements in ML and AI to search relevance and personalization
  • ML Engineers working on different aspects of relevance and personalization
  • experimentation oriented and runs frequent A/B tests to improve the relevance
  • applied research oriented and regularly publishes work at KDD, CIKM and WSDM Conferences