Senior Machine Learning Engineer, Trust Foundational Modeling

Airbnb was born in 2007 when two Hosts welcomed three guests to their San Francisco home, and has since grown to over 4 million Hosts who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Every day, Hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.

The Community You Will Join:

Everyone at Airbnb thinks about trust, but our team obsesses over it daily. At the core of trust is safety, and thus we spend a significant amount of our time and energy keeping the community safe. The Trust team is responsible for protecting our community and platform from fraud while also ensuring our hosts, guests, homes, and experiences meet our high standards. We constantly work to fight against online fraud (such as monetary loss, compromised accounts, spam and scam in messages, fake inventory, etc.) as well as offline fraud (theft, property damage, personal safety, etc.). We also work on onboarding and screening of users, and think about complex topics like identity and reputation to ensure that every interaction with Airbnb helps build trust in us and our community. Trust Engineering is responsible for the technology vision and development of a complex stack that runs on every key interaction on the platform.

The Difference You Will Make:

As a technical individual contributor, you will partner closely with our collaborators across the broader technical organization. All individual contributors at Airbnb are Software Engineers which means we expect you to be hands on and contribute code.

A Typical Day: 

As a machine learning engineer in Trust, your contributions will span a variety of shapes:

• Work with large-scale structured and unstructured data to build and continuously improve cutting-edge machine learning models for Airbnb’s product, business, and operational use cases. Some recent works include:
  
  • Supervised model ensembles that include multiple DNNs and graph-based models
  • Contextual Decision engine 
  • Unsupervised clustering models for natural language understanding
  • LLM experimentation with label taxonomy and manual agent automation
  • Optimizing ranking algorithms for search
• Collaborate with a wide variety of business functions to predict and prevent physical safety and property damage incidents.
• Develop new holistic machine learning model detection strategies by partnering with other teams across the Trust Organization.
• Work collaboratively with cross-functional partners, …