Machine Learning Engineer II (Underwriting ML)

<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> </div> <div> <p>On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout. 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 user behavior and macroeconomic conditions evolve.</p> <p> </p> <p><strong>What you’ll do</strong></p> <p>- You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential 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 will help 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</p> <p>- You will collaborate across Engineering, Risk 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><strong>What we look for</strong></p> <p>- You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field.</p> <p>- Strong Python skills and experience writing production-quality code.</p> <p>- Experience building and evaluating models for 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, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).</p> <p>- Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day