Senior Machine Learning Engineer (Fraud)

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. 1 On the ML Fraud team, you’ll build and improve machine learning systems that make real-time transaction decisions, protecting consumers and merchants while balancing fraud loss, customer experience, and conversion. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as fraud patterns evolve. What you’ll do - You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will 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. - You will 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 - You have 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,