Senior Machine Learning Engineer - Fraud

Responsibilities

• Investigate fraud patterns and model errors to identify new signals, improve detection, and expand coverage.
• Develop training datasets and predictive features while addressing incomplete labels, class imbalance, data leakage, and changing fraud behavior.
• Design, train, tune, and evaluate models including gradient-boosted trees and neural networks.
• Design experiments comparing model performance across time periods and customer segments using detection and false-positive metrics.
• Build reproducible data and training pipelines for efficient experimentation and iteration.
• Deploy models with Engineering and ML Infrastructure partners while balancing detection quality, latency, cost, and reliability.
• Lead ML projects independently from initial experimentation through deployment, model release, and ongoing improvement.
• Evaluate production model impact using real-world customer outcomes and explore LLMs and Generative AI for fraud detection, prevention, and investigation.

Requirements

• 7+ years of professional experience in machine learning, applied science, or software engineering for ML.
• Hands-on experience designing, training, tuning, deploying, and evaluating machine learning models in production.
• Strong machine learning and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing underperforming models.
• 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 across time periods or populations.
• Strong Python skills and SQL proficiency for training and evaluation data.
• Hands-on experience with ML frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents.
• Experience independently leading open-ended ML projects through deployment and coordinating requirements and releases with Data Science, Product, and Engineering.
• Preferred experience in fraud or risk modeling, including delayed feedback and balancing fraud detection with legitimate-user friction.
• Preferred experience developing models that generalize across customers and using graph-based systems to extract predictive signals.
• Preferred experience applying learned representations, transformers, or foundation models to production ML use cases.

Benefits

• Comprehensive medical, dental, vision, and 401(k) benefits.
• Additional compensation may include equity and/or commission, depending on the position offered.
• Pay and benefits are based on factors including scope, responsibilities, experience, skills, and location and may change under applicable plans.