Software Engineer, Trust

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 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:

Imagine getting off a long international flight, standing in the freezing rain, attempting to contact your Airbnb host, only to realize that they are not responding because the Airbnb that you booked was not real. The Listing Integrity team’s ultimate goal is to prevent experiences like this by proactively detecting and removing fraudulent listings (Homes, Experiences, and Services) so that guests can search and book on Airbnb with confidence. The team uses data driven heuristics, machine learning models, and customer service operations in order to accomplish this goal.

The Difference You Will Make:

As a Software Engineer on the Listing Integrity team, you will be working with data scientists, designers, product managers, and customer service operations to innovate new ways we can stop bad actors in the ever evolving fraudulent listing creation and financial losses associated with it by collaborating across team boundaries.

On this team, you must have the curiosity to dig deep into various end to end systems in order to understand how and where the fraud occurs. Your curiosity will be rewarded with finding projects that have outsized impacts on decreasing fraud losses and protecting Airbnb users from bad experiences while on vacation.

A Typical Day:

• Work with large scale back end systems to detect fraudsters, using rules and connecting to productionalized machine learning models.
• Work collaboratively with cross-functional partners including machine learning engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for fraud detection and mitigation.
• Building out tooling and automation for operations to more effectively reduce fraud.
• Building user challenges to prevent fraudulent listings while reducing good user impact.
• Examples include: location verification user challenges, automating fraud investigation, improving operations tooling, building data pipelines for machine learning, and reducing fraud at scale.