About the job
Embrace the Future of Commerce with Whatnot!
Whatnot is revolutionizing live shopping across North America and Europe, offering a vibrant platform to buy, sell, and explore your favorite items. We are redefining e-commerce by seamlessly blending community, shopping, and entertainment into a unique marketplace tailored for you. As a part of a remote co-located team, our innovation-driven culture is firmly rooted in our core values. With operational hubs in the US, UK, Germany, Ireland, and Poland, we are collectively shaping the future of online marketplaces.
From fashion and beauty to electronics and collectibles like trading cards, comic books, and live plants, our live auctions cater to diverse interests.
And this is just the beginning! As one of the fastest-growing marketplaces, we seek innovative, forward-thinking problem solvers across all areas. Follow our latest updates on our news and engineering blogs and be part of our mission to empower individuals to turn their passions into thriving businesses, while fostering connections through commerce.
Role Overview
The Fraud Experience team is dedicated to building intelligent, real-time systems that protect Whatnot’s marketplace from fraudulent activities. We focus on designing, developing, and deploying end-to-end, machine-learning driven systems that proactively identify and combat fraud, ensuring a transparent, user-centered enforcement approach.
Your Responsibilities
Architect and lead the development of comprehensive fraud detection, prevention, and intervention systems, integrating machine learning, backend, and client-side components.
Construct intelligent user graphs to analyze behavioral patterns, identify collusion networks, and reveal account connectivity at scale.
Design, train, and implement both traditional ML and LLM-powered models to detect fraudulent behavior across user interactions, payments, and marketplace activities.
Create scalable data pipelines and real-time inference systems able to handle high-volume, low-latency machine learning tasks.
Develop human-in-the-loop systems that continually enhance detection accuracy and adapt to evolving adversarial strategies.
Conduct in-depth behavioral and adversarial data analysis to identify emerging fraud trends and drive continuous improvement.

