Senior Machine Learning Engineer, Fraud

What you'll do

• Investigate fraud patterns and model errors to identify new signals and improve detection coverage.
• Develop training datasets and predictive features while addressing issues like class imbalance and data leakage.
• Design, train, and tune models using both traditional methods and modern neural network architectures.
• Build data and training pipelines to support reproducible experiments and efficient feature iteration.
• Deploy models in production while balancing detection quality, latency, cost, and reliability.
• Explore the application of LLMs and Generative AI to improve fraud detection and investigation.
• Lead ML projects independently from initial experimentation through coordination with Product and Engineering teams.

What we're looking for

• 7+ years of professional experience in machine learning, applied science, or software engineering for ML.
• Experience designing, training, tuning, and deploying models while measuring improvements in production performance or business metrics.
• Strong ML and statistical fundamentals including feature engineering, experiment design, model evaluation, and diagnosing underperformance.
• Proficiency in both traditional and modern ML methods, such as gradient-boosted trees and neural networks.
• Experience constructing training datasets and addressing issues like label quality, data leakage, and class imbalance.
• Strong Python skills, SQL proficiency, and hands-on experience with frameworks like PyTorch, scikit-learn, or XGBoost.
• Experience independently leading ML projects from problem identification through deployment and cross-functional coordination.
• Fraud or risk modeling experience, graph-based systems, and advanced modeling approaches like transformers (preferred).