Machine Learning Engineer II (Underwriting ML)
Affirm · Remote US
About this role
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 repayment risk and expected value for every Affirm checkout. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea → prototype → production, and keep them healthy with strong measurement and monitoring as user behavior and macroeconomic conditions evolve. **What you’ll do** - 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 (partnering with data and platform teams as needed) - Prototype new modeling ideas and features; run offline experiments; drive best-performing approaches into production with appropriate **risk controls** - Help productionize models by integrating into **batch and/or real-time decision systems** and improving reliability, latency, and operational robustness - Instrument and monitor **model and data health**; help define **retraining/backtesting** workflows - Collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to technical and non-technical audiences **What we look for** - **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 (**PyTorch preferred**) - Experience with distributed data processing / 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) - Proficiency with AI-powered developer tools (e.g., **Claude Code, Cursor**, or similar) to accelerate iteration, debugging, and code quality - Ability to take a simple problem/business scenario into a solution that spans multiple software components—writing clear, well-tested, extensible code - Comfort navigating a large codebase, debugging others’ code, and providing feedback via code reviews - Demonstrated ownership of growth—proactively seeking feedback from your team, manager, and stakeholders - Strong verbal and written communication skills for collaboration with a global engineering team - Requires either equivalent practical experience or a **Bachelor’s degree** in a related field **Compensation & location** - Pay Grade: **L** - Equity Grade: **6** - **#LI-Remote** (remote-first; most roles are remote within the country of employment) - USA base pay range (CA, WA, NY, NJ, CT): **$165,000 - $225,000** per year - USA base pay range (all other U.S. states): **$146,000 - $206,000** per year Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness, and tech spending, and benefits (including **100% subsidized medical coverage**, dental and vision fo
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