Machine Learning Engineer II (Underwriting ML)

Affirm

5d•CA$133,000 - CA$183,000•Remote

About The Position

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.

Requirements

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 (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.
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.
Comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.
Experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.
Strong verbal and written communication skills that support effective collaboration with our global engineering team.
Equivalent practical experience or a Bachelor’s degree in a related field.

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, partnering with data and platform teams when needed.
Prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
Help 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 to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.