Senior Machine Learning Engineer (Fraud)

<div class="content-intro"><p>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.</p></div><div> <p data-pm-slice="1 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.</p> <p><br> </p> <p>What you’ll do</p> <p>- You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data</p> <p>- You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. </p> <p>- You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. </p> <p>- You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. </p> <p>- You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve.</p> <p>- Identify and implement foundational improvements to how the team builds models.</p> <p>- 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. </p> <p> </p> <p>What we look for</p> <p>- 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.</p> <p>- Track record of delivering high impact machine learning models in a low latency live setting</p> <p>- Strong Python skills and experience writing production-quality code.</p> <p>- Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar). </p> <p>- Experience with a deep learning framework (PyTorch preferred).</p> <p>- Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar). </p> <p>- Experience with ML lifecycle tooling for training orchestration,