Machine Learning Engineer II (Underwriting ML)

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. In this role, you will build and improve machine learning systems that make real-time transaction decisions, assessing repayment risk and expected value for Affirm checkouts.

Responsibilities:

• You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential 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 will help 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
• 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

Requirements:

• You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field
• Strong Python skills and experience writing production-quality code
• Experience building and evaluating models for classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar)
• You have 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
• You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews
• Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders
• You have strong verbal and written communication skills that support effective collaboration with our global engineering team
• This position requires either equivalent practical experience or a Bachelor's degree in a related field
• 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 as part of day-to-day development workflows