About the company
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.
Responsibilities
- Develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data
- Build and scale feature pipelines and training datasets from proprietary and third-party signals
- Prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production
- Productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness
- Instrument and monitor model and data health, and help define retraining/backtesting workflows
- Collaborate across Engineering, Risk Analytics, Product, and ML Platform
Requirements
- 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 (LightGBM/XGBoost/CatBoost or similar)
- Experience with a deep learning framework (PyTorch preferred)
- Experience with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar)
- Experience with ML lifecycle tooling (Kubeflow, Airflow, MLflow, or equivalent)
- Proficient in using AI-powered developer tools (Claude Code, Cursor, or similar)
- Comfortable navigating a large code base and providing feedback through code reviews
- Strong verbal and written communication skills
- Bachelor's degree in a related field or equivalent practical experience
Conditions
- USA base pay range (CA, WA, NY, NJ, CT) per year: $165,000 - $225,000
- USA base pay range (all other U.S. states) per year: $146,000 - $206,000
- Remote-first with flexibility built in
- Health coverage at no cost, spending stipends, flexible time off, ESPP