Senior Machine Learning Engineer - Fraud
Plaid · San Francisco HQ
About this role
**Senior Machine Learning Engineer - Fraud** **About the Role** Join Plaid's Fraud Data team to develop machine learning systems that power fraud detection products. You'll work across the full ML lifecycle—from discovering signals and experimenting with models to deploying and optimizing them in production. **Key Responsibilities** • Investigate fraud patterns and model errors to identify new signals and improve detection coverage • Develop training datasets and predictive features, addressing challenges like incomplete labels and class imbalance • Design, train, and tune models using gradient-boosted trees, neural networks, and modern ML methods • Design experiments to test features and models across time periods and customer segments • Build data and training pipelines supporting reproducible experiments • Deploy models with Engineering and ML Infrastructure partners • Lead ML projects independently from conception through production release • Explore how LLMs and Generative AI can improve fraud detection **Required Qualifications** • 7+ years professional experience in ML, applied science, or ML software engineering • Hands-on experience designing, training, tuning, and deploying models • Strong ML and statistical fundamentals (feature engineering, experiment design, model evaluation) • Experience with both traditional and modern ML methods • Expertise constructing training datasets and addressing data quality challenges • Strong Python, SQL proficiency, and experience with ML frameworks (PyTorch, scikit-learn, XGBoost, etc.) • Experience independently leading ML projects from problem definition through deployment **Nice-to-Have** • Fraud or risk modeling experience • Experience with graph-based systems for fraud detection • Models that generalize across customers with different patterns • Experience with transformers, learned representations, or foundation models **About Plaid** Plaid powers financial connectivity for millions of users across 12,000+ institutions in the US, Canada, UK, and Europe. We're committed to building a diverse team and encourage applications even if your experience doesn't fully match the description. *Equal Opportunity Employer | Reasonable accommodations available*
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