Senior Machine Learning Engineer - Fraud
$229k - $315k • Remote • San Francisco HQ • FullTime
Posted 1mo ago
Job Location
San Francisco HQ
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
Plaid is seeking a Senior Machine Learning Engineer to join their Fraud Data team. This role will focus on developing and deploying machine learning systems to enhance Plaid's fraud detection products. You will work across the full data science and machine learning lifecycle, from identifying new signals and experimenting with models to deploying and optimizing them in production. The goal is to continuously learn from real-world performance and customer feedback to protect customers from evolving fraud threats. You will lead projects from initial experiments through model deployment and ongoing improvement, identifying predictive patterns in Plaid's network data.
Responsibilities
- Investigate fraud patterns and model errors to identify new signals, improve detection, and expand coverage.
- Develop training datasets and predictive features, addressing challenges like incomplete labels and class imbalance.
- 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, evaluating performance across different time periods and customer segments.
- Build data and training pipelines for reproducible experiments and efficient iteration.
- Deploy models with Engineering and ML Infrastructure partners, balancing quality, latency, cost, and reliability.
- Independently lead ML projects, coordinating priorities and evaluation metrics with Data Science and Product.
- Build hands-on machine learning expertise across the full ML lifecycle.
- Take models from initial experimentation through production and evaluate their impact.
- Develop experience building and scaling reliable ML systems in production.
- Explore how LLMs and Generative AI can improve fraud detection, prevention, and investigation.
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 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.
- Strong Python skills and SQL proficiency.
- Hands-on experience with ML frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents.
- Experience independently leading ML projects from problem definition through deployment.
- Fraud or risk modeling experience is strongly preferred.
- Experience developing models that generalize across customers with different data and behavior patterns.
- Experience using graph-based systems to extract predictive signals and improve fraud model performance.
- Experience applying newer modeling approaches, such as learned representations, transformers, or foundation models.