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.

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.

What you’ll do

- 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.

What we look for

- 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).

- Experience with a deep learning framework (PyTorc