Senior Machine Learning Engineer (Fraud)

About this role

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. 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).