Senior Machine Learning Engineer - Fraud

The role

You will develop models to improve fraud detection for customers. You will identify predictive patterns in Plaid's network data and lead projects from initial experiments through model deployment and ongoing improvement.

What you will do

• Investigate fraud patterns and model errors to identify new signals
• Develop training datasets and predictive features
• Design, train, and tune models using traditional and modern ML methods
• Design experiments to test features and models
• Build data and training pipelines
• Deploy models with Engineering and ML Infrastructure partners
• Independently lead ML projects

Required qualifications

• 7+ years of professional experience in machine learning, applied science, or software engineering for ML, including hands-on model development and deployment
• Hands-on experience designing, training, tuning, and deploying models, and measuring improvements in production performance or business metrics
• Strong ML and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing why a model underperforms
• Strong understanding of the strengths, limitations, and applications for both traditional and modern ML methods, including gradient-boosted trees and neural networks
• Experience constructing training datasets and addressing label quality, data leakage, class imbalance, and generalization across time periods or populations
• Strong Python skills
• SQL proficiency for working with training and evaluation data
• Hands-on experience with ML frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents
• Experience independently leading ML projects from an open-ended problem through deployment

Nice to have

• 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

Benefits

• Medical
• Dental
• Vision
• 401(k)

About the company

Plaid powers the tools millions of people rely on to live a healthier financial life. The company works with thousands of companies to make it easy for people to connect their financial accounts to apps and services.