Senior Machine Learning Engineer, Fraud

What you'll do

• Investigate fraud patterns and model errors to identify new signals and expand detection coverage.
• Develop training datasets and predictive features while addressing class imbalance and data leakage.
• Design, train, and tune models using gradient-boosted trees, neural networks, and other modern ML methods.
• Build data and training pipelines to support reproducible experiments and efficient model iteration.
• Deploy models in production while balancing detection quality, latency, cost, and reliability.
• Lead ML projects independently from initial experimentation through model release and production monitoring.
• Explore the application of LLMs and Generative AI to improve fraud detection and investigation.

What we're looking for

• 7+ years of professional experience in machine learning, applied science, or software engineering for ML.
• Hands-on experience designing, training, tuning, and deploying models while measuring production performance or business metrics.
• Strong ML and statistical fundamentals including feature engineering, experiment design, and model evaluation.
• Proficiency in Python, SQL, and ML frameworks such as PyTorch, scikit-learn, or XGBoost.
• Experience constructing training datasets and addressing label quality, data leakage, and class imbalance.
• Experience independently leading ML projects from problem definition through deployment and cross-functional coordination.
• Experience with traditional and modern ML methods, including gradient-boosted trees and neural networks.
• Fraud or risk modeling experience, including familiarity with fraud patterns and delayed feedback (preferred).