Machine Learning Engineer II (Underwriting ML)

Affirm is a financial technology company that provides transparent, predictable payment options over time, with no hidden fees or surprises. The company uses advanced technology and analytics to serve a broader population than traditional lenders.

As a Machine Learning Engineer II on the Underwriting ML team, you will build and improve machine learning systems that make real-time transaction decisions, assessing repayment risk and expected value for every Affirm checkout. You will work with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and maintain their health through measurement and monitoring.

• 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 with appropriate risk controls.
• Productionize models by integrating into batch and/or real-time decision systems, improving 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 to define requirements, evaluate tradeoffs, and communicate results to technical and non-technical audiences.

• 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 with gradient-boosted decision trees like LightGBM/XGBoost/CatBoost.
• 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 for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow).
• Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor) to accelerate iteration, debugging, and code quality.
• Ability to take a simple problem or business scenario into a solution that interacts with multiple software components, writing clear, well-tested, and extensible code.
• Comfortable navigating a large code base, debugging others' code, and providing feedback through code reviews.
• Strong verbal and written communication skills for effective collaboration with a global engineering team.

Base pay ranges: $165,000 - $225,000 per year for CA, WA, NY, NJ, CT; $146,000 - $206,000 per year for all other U.S. states. Total compensation may include equity rewards, monthly stipends for health, wellness, and tech spending, and benefits including 100% subsidized medical coverage, dental, and vision for employees and dependents. Additional benefits include flexible time off, generous holiday calendars, and an employee stock purchase plan (ESPP).

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