Senior Machine Learning Engineer - Fraud

Responsibilities

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

Requirements

• 7+ years of professional experience in machine learning, applied science, or software engineering for machine learning, including hands-on model development and deployment.
• Experience designing, training, tuning, and deploying models and measuring improvements in production performance or business metrics.
• Strong machine learning and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing model performance.
• Understanding of traditional and modern machine learning 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.
• Hands-on experience with machine learning frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents.
• Experience independently leading machine learning projects from open-ended problem definition through deployment and coordinating requirements and model releases with Data Science, Product, and Engineering.

Nice-to-Haves

• 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 and uncover fraud patterns.
• Experience applying learned representations, transformers, or foundation models to production machine learning use cases.

Compensation and Benefits

• Annual salary range: $228,960–$315,360.
• Additional compensation may include equity and/or commission.
• Comprehensive benefits include medical, dental, vision, and 401(k).