Senior Machine Learning Engineer (Fraud)

Join Plaid's Fraud Data team as a Senior Machine Learning Engineer. In this role, you will develop models to improve fraud detection for our customers, investigate fraud patterns, design experiments, build data pipelines, and deploy models. You will have the opportunity to take ownership, solve complex problems, and make a meaningful impact in a fast-paced environment.

• Identifying predictive patterns in Plaid's network data and leading projects from initial experiments through model deployment and ongoing improvement.
• Investigating fraud patterns and model errors to identify new signals, improve detection, and expand coverage across customers and use cases.
• Designing, training, and tuning models using traditional and modern ML methods, and evaluating newer architectures against existing approaches.

• Strong Python skills, SQL proficiency for working with training and evaluation data, and hands-on experience with ML frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents
• Experience constructing training datasets and addressing label quality, data leakage, class imbalance, and generalization across time periods or populations
• 7+ years of professional experience in machine learning, applied science, or software engineering for ML, including hands-on model development and deployment
• Experience independently leading ML projects from an open-ended problem through deployment, coordinating requirements and model releases with Data Science, Product, and Engineering
• Strong ML and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing why a model underperforms
• Hands-on experience designing, training, tuning, and deploying models, and measuring improvements in production performance or business metrics
• Strong understanding of the strengths, limitations, and applications for both traditional and modern ML methods, including gradient-boosted trees and neural networks
• We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description
• Strongly preferred: Fraud or risk modeling experience, including familiarity with fraud patterns, delayed feedback, and the tradeoff between fraud detection and legitimate-user friction
• Experience developing models that generalize across customers with different data and behavior patterns
• Experience using graph-based systems to extract predictive signals, uncover fraud patterns, and improve fraud model performance
• Experience applying newer modeling approaches, such as learned representations, transformers, or foundation models, to improve a production ML use case