Senior Machine Learning Engineer, Trust

Airbnb is a global platform that connects hosts and guests, and they are seeking a Senior Machine Learning Engineer to join their Trust team. The role involves designing and building ML solutions to protect the community from fraud while ensuring a seamless experience for users.

Responsibilities:

• 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

Requirements:

• 5–10 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
• A Bachelor's, Master's, or PhD in CS/ML or a related field
• Exposure to the Trust and Risk domain (e.g., fraud detection, anomaly detection, identity, account integrity) is a plus