Machine Learning Engineer, Fraud

Whatnot•San Francisco, CA

1d•Hybrid

About The Position

🚀 Join the Future of Commerce with Whatnot! Whatnot is the largest live shopping platform in North America and Europe to buy, sell, and discover the things you love. We’re re-defining e-commerce by blending community, shopping, and entertainment into a community just for you. As a remote co-located team, we’re inspired by innovation and anchored in our values . With hubs in the US, UK, Germany, Ireland, and Poland, we’re building the future of online marketplaces –together. From fashion, beauty, and electronics to collectibles like trading cards, comic books, and even live plants, our live auctions have something for everyone. And we’re just getting started! As one of the fastest growing marketplaces , we’re looking for bold, forward-thinking problem solvers across all functional areas. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business, and bring people together through commerce. 💻 Role Design, train, and deploy both traditional ML and LLM-powered models to detect fraudulent behaviors across users, payments, and marketplace interactions. Lead the end-to-end architecture of fraud detection, prevention, and intervention systems — balancing platform security with a seamless user experience. Build intelligent user graphs to model behavioral patterns, collusion networks, and account connectivity. Develop scalable data pipelines and real-time inference systems supporting high-volume, low-latency ML workloads. Conduct deep behavioral and adversarial data analysis to uncover fraud trends and continuously improve detection accuracy. Partner cross-functionally with Trust & Safety, Payments, and Infrastructure teams to develop features, labels, and model evaluation pipelines. Implement model monitoring and drift detection systems to ensure reliability and responsiveness. Contribute to fraud risk orchestration, combining rules, models, and heuristics for decision automation. Define and track key metrics and dashboards for fraud detection effectiveness (e.g., precision, recall, false-positive rate, latency). Stay ahead of emerging fraud tactics and continuously translate insights into adaptive, production-ready systems. We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our SF, NYC, LA OR SEA hubs.

Requirements

Bachelor’s degree in Computer Science, a related field, or equivalent work experience.
2–6 years of experience in machine learning or software engineering, ideally in risk, fraud, or trust & safety domains.
Strong proficiency in Python and ML libraries (e.g., scikit-learn, PyTorch, LightGBM).
Solid backend development skills and experience deploying ML models to production (batch or real-time).
Experience in data analysis and ETL (SQL, Spark, DBT) for data pipeline building.
Familiarity with fraud detection techniques such as chargeback prediction, anomaly detection, or graph-based modeling.
Experience with data orchestration frameworks (Dagster, Kubeflow) and feature store design.
Ability to translate business risk into measurable ML solutions and collaborate across diverse

Responsibilities

Design, train, and deploy both traditional ML and LLM-powered models to detect fraudulent behaviors across users, payments, and marketplace interactions.
Lead the end-to-end architecture of fraud detection, prevention, and intervention systems — balancing platform security with a seamless user experience.
Build intelligent user graphs to model behavioral patterns, collusion networks, and account connectivity.
Develop scalable data pipelines and real-time inference systems supporting high-volume, low-latency ML workloads.
Conduct deep behavioral and adversarial data analysis to uncover fraud trends and continuously improve detection accuracy.
Partner cross-functionally with Trust & Safety, Payments, and Infrastructure teams to develop features, labels, and model evaluation pipelines.
Implement model monitoring and drift detection systems to ensure reliability and responsiveness.
Contribute to fraud risk orchestration, combining rules, models, and heuristics for decision automation.
Define and track key metrics and dashboards for fraud detection effectiveness (e.g., precision, recall, false-positive rate, latency).
Stay ahead of emerging fraud tactics and continuously translate insights into adaptive, production-ready systems.

Benefits

Generous Holiday and Time off Policy
Health Insurance options including Medical, Dental, Vision
Work From Home Support
Home office setup allowance
Monthly allowance for cell phone and internet
Care benefits
Monthly allowance for wellness
Annual allowance towards Childcare
Lifetime benefit for family planning, such as adoption or fertility expenses
Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
Monthly allowance to dogfood the app
Parental Leave
16 weeks of paid parental leave + one month gradual return to work company leave allowances run concurrently with country leave requirements which take precedence.