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
Collaborate with product managers, data scientists, software engineers, and: operations teams to identify opportunities, scope ML solutions, and refine requirements for new or improved Trust models.
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.
Investigate emerging fraud patterns and threat signals with your teammates,: and develop ML-based detections and tools that enable faster, more accurate responses.
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.
Participate in code reviews, design discussions, and cross-team: collaborations to contribute to a high-quality ML engineering culture.
What they're looking for
- Work closely with trust defense and platform teams to adapt models and: systems to an evolving landscape of fraud attacks.
- + years of industry experience in applied Machine Learning, with a track: record of building and productionizing models at scale.
- Strong programming skills in Python (required) and familiarity with Scala, Java, or equivalent.
- Solid understanding of Machine Learning best practices: e.g., training/serving skew minimization, A/B testing, feature engineering, model selection — and algorithms such as gradient boosted trees, neural networks, transformers, and deep learning.
