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Plaid is seeking a Senior Machine Learning Engineer to join the Fraud Data team in San Francisco. The Fraud Data team builds machine learning systems that power Plaid's fraud detection products, leveraging insights from Plaid's network of 12,000 financial institutions across the US, Canada, UK, and Europe to identify and stop fraud before it happens.
In this role, you will develop models that improve fraud detection for Plaid's customers, working across the full data science and machine learning lifecycle—from discovering new signals and experimenting with models to deploying and optimizing them in production. You will investigate fraud patterns and model errors to identify new signals, improve detection, and expand coverage across customers and use cases.
Key responsibilities include:
- Investigating fraud patterns and model errors to identify new predictive signals and improve detection coverage
- Developing training datasets and predictive features, addressing challenges such as incomplete labels, class imbalance, data leakage, and changing fraud behavior
- Designing, training, and tuning models using traditional and modern ML methods, including gradient-boosted trees and neural networks
- Designing experiments to test features and models, comparing performance across time periods and customer segments
- Building data and training pipelines that support reproducible experiments and efficient iteration
- Deploying models in coordination with Engineering and ML Infrastructure partners, balancing detection quality, latency, cost, and reliability
- Independently leading ML projects from initial experimentation through production, coordinating with Data Science, Product, and Engineering teams
- Exploring how LLMs and Generative AI can improve fraud detection, prevention, and investigation
You will take models from initial experimentation through production, evaluate their impact using real-world customer outcomes, and develop experience building and scaling reliable ML systems in production.
REQUIREMENTS:
- 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 model underperformance
- 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, and 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, coordinating requirements and model releases with Data Science, Product, and Engineering
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
