<div class="content-intro"><p><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">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 connect with communities in a more authentic way.</span></p></div><p><strong>The Community You Will Join:</strong></p> <p>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 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.</p> <p>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.</p> <p><strong>The Difference You Will Make:</strong></p> <p>As a Senior Machine Learning Engineer on the Trust team, you will actively contribute code and ideas that shape the ML systems protecting millions of Airbnb users. You'll own and deliver ML projects end-to-end — from designing and training models to productionizing and operating them at scale, while collaborating closely with cross-functional partners.</p> <p>You'll tackle real-world challenges such as account takeover, fake accounts, payment fraud, and bot detection. Your work will help reduce risks posed by bad actors while ensuring the platform remains seamless and welcoming for everyone else. As you develop your skills, you'll see the tangible impact of your models, helping real users stay safe and confident as they travel, host, and connect on Airbnb.</p> <p><strong>A Typical Day: </strong></p> <ul> <li>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.</li> <li>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.</li> <li>Investigate emerging fraud patterns and threat signals with your teammates, and develop ML-based detections and tools that enable faster, more accurate responses.</li> <li>Write, review, and ship clean, testable code — whether training a new model, improving an existing pipeline, or optimizing a feature for scalability and reliability.</li> <li>Work with large-scale structured and unstructured data to continuously improve ML models for Airbnb product, business, and operational use cases.</li> <li>Participate in code reviews, design discussions, and cross-team collaborations to contribute to a high-quality ML engineering culture.</li> <li>Work closely with trust defense and platform teams to adapt models and systems to an evolving landscape of fraud attacks.</li> <li>Build and optimize top-of-funnel fraud detection models to improve early detection and strengthen system-wide fraud defenses.</li> <li>Explore, prototype, and productionize innovative ML models and techniques to detect and counter emerging fraud vectors.</li> <li>Explore and leverage LLMs and GenAI technologies to advance fraud and abuse detection, investigation, and response capabilities.</li> </ul> <p><strong>Your Expertise:</strong></p> <ul> <li>5+ years of industry experience in applied Machine Learning, with a track record of building and productionizing models at scale.</li> <li>Strong programming skills in Python (required) and familiarity with Scala, Java, or equivalent.</li> <li>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.</li> <li>Experience with ML frameworks and tooling such as TensorFlow, PyTorch, or equivalent.</li> <li>Experience with data engineering and building end-to-end ML pipelines, including both batch and real-time systems.</li> <li>Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high-volume data pipelines, efficient algorithms).</li> <li>Experience with test-driven development, incremental delivery, and deployment practices.</li> <li>Exposure to the Trust and Risk domain (e.g., fraud detection, anomaly detection, identity, account integrity) is a plus.</li> <li>A Bachelor's, Master's, or PhD in CS/ML or a related field.</li> <li>Experience designing and implementing ML evaluation frameworks, including ground truth, offline and online metrics, and model calibration.</li> <li>Experience with or strong interest in LLMs and GenAI tech