The opportunity
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…
What you'll do
Frame and prototype ML and agentic solutions for problems that do not yet: have an established approach, in partnership with product managers, data scientists, and front line defense teams.
Design, build, and productionize end-to-end Machine Learning pipelines: including feature engineering, model training, evaluation, and deployment — for both batch and real-time use cases.
Build and improve abuse behavior detection that generalizes across defenses.
Design, launch, and iterate on AI agents that automate trust decisions,: including orchestration, tool interfaces, and the guardrails that hold quality steady as autonomy increases.
Build benchmarks, evaluation harnesses, and instrumentation that let us: measure agentic and model decision quality objectively, and use them to drive real improvements.
Develop specialized models for trust and safety use cases, and use LLMs and: AI agents to accelerate how we build models.
What they're looking for
- Write, review, and ship clean, testable code: whether training a new model, improving an existing pipeline, or optimizing a feature for scalability and reliability.
- Work with large-scale structured and unstructured data to continuously: improve ML models for Airbnb product, business, and operational use cases.
- Partner with front line defense teams to validate solutions through: experiments and holdouts, and quantify their impact on business and operational metrics.
- Participate in code reviews, design discussions, and cross-team: collaborations to contribute to a high-quality ML engineering culture.
