Senior Machine Learning Engineer (Fraud)

What you'll do

• Lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data
• Build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed
• Prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls
• Productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness
• Instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve
• Identify and implement foundational improvements to how the team builds models
• Collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences

What we look for

• 6+ years experience researching, training, tuning and launching ML models at scale. Relevant PhD can count for up to 2 years of experience
• Track record of delivering high impact machine learning models in a low latency live setting
• Strong Python skills and experience writing production-quality code
• Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar)
• Experience with a deep learning framework (PyTorch preferred)
• Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar)
• Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms)
• Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality
• Mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code
• Comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews
• Demonstrated ownership of growth, proactively seeking feedback from team, manager, and stakeholders
• Strong verbal and written communication skills that support effective collaboration with global engineering team