Senior Machine Learning 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 conne...

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 developing the technology that helps protect our community and platform from fraud wh...

You'll work side-by-side with talented product managers, data scientists, software engineers, fraud intelligence, and operations teams. Together, you'll design and build ML solutions that have direct, meaningful impact on user trust, business success, and the global Airbnb community.

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.
Work closely with trust defense and platform teams to adapt models and systems to an evolving landscape of fraud attacks.

5+ 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.
Experience with ML frameworks and tooling such as TensorFlow, PyTorch, or equivalent.
Experience with data engineering and building end-to-end ML pipelines, including both batch and real-time systems.
Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high-volume data pipelines, efficient algorithms).
Experience with test-driven development, incremental delivery, and deployment practices.
Exposure to the Trust and Risk domain (e.g., fraud detection, anomaly detection, identity, account integrity) is a plus.
A Bachelor's, Master's, or PhD in CS/ML or a related field.