Description
We believe that the way people interact with their finances will drastically improve in the next few years. We're dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products.
The Fraud Data team at Plaid builds machine learning systems that power Plaid's fraud detection products. As a Senior Machine Learning Engineer on Plaid's Fraud Data team, you will develop models that improve fraud detection for our customers. You will identify predictive patterns in Plaid's network data and lead projects from initial experiments through model deployment and ongoing improvement.
Responsibilities:
- Investigate fraud patterns and model errors to identify new signals, improve detection, and expand coverage across customers and use cases.
- Develop training datasets and predictive features, addressing challenges such as incomplete labels, class imbalance, data leakage, and changing fraud behavior.
- Design, train, and tune models using traditional and modern ML methods, including gradient-boosted trees and neural networks.
- Design experiments to test features and models, comparing performance across time periods and customer segments.
- Build data and training pipelines that support reproducible experiments and efficient iteration on features and models.
- Deploy models with Engineering and ML Infrastructure partners, balancing detection quality, latency, cost, and reliability.
- Independently lead ML projects, agreeing on priorities and evaluation metrics with Data Science and Product and coordinating work through model release.
Qualifications:
- 7+ years of professional experience in machine learning, applied science, or software engineering for ML.
- Hands-on experience designing, training, tuning, and deploying models.
- Strong ML and statistical fundamentals, including feature engineering, experiment design, and model evaluation.
- Strong understanding of traditional and modern ML methods.
- Experience constructing training datasets and addressing label quality, data leakage, and class imbalance.
- Strong Python skills, SQL proficiency, and hands-on experience with ML frameworks.
Nice-to-Have:
- Fraud or risk modeling experience.
- Experience developing models that generalize across customers with different data and behavior patterns.
- Experience using graph-based systems to extract predictive signals.
- Experience applying newer modeling approaches, such as learned representations or foundation models.
Compensation:
- $228,960 – $315,360
Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k).