Airbnb careers remote

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 JoinEveryone 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 MakeAs a senior technical individual contributor, you will partner closely with our senior leaders across the broader technical organization. Although you will be at one of our highest levels of seniority, all individual contributors at Airbnb are Software Engineers which means we expect you to be hands on and contribute code.A Typical DayYour contributions take a variety of shapes:

Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases.
Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.
Work closely with other trust defense and platform teams to tackle the changing landscape of fraud attacks.
Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
Examples include: Anomaly detection models, ML models for continuous risk evaluation.