ML Engineer II: Real-Time Credit Underwriting

Affirm; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Affirm is building underwriting ML systems that decide real-time transaction risk and expected value at checkout. You will work with ML engineers, data, and platform teams to move models from idea to production, while monitoring health as user behavior and macro conditions evolve.

You will develop underwriting models, build feature pipelines, prototype ideas, and productionize them with strong risk controls.

Qualifications

• 2+ years of experience as a machine learning engineer or a PhD in a relevant field.
• Strong Python skills and production-quality code.
• Experience with gradient-boosted trees (LightGBM/XGBoost/CatBoost) or similar.
• Experience with PyTorch.
• Experience with distributed data processing (Spark).
• Experience with ML lifecycle tooling (Kubeflow, Airflow, MLflow).
• Proficient in using AI-powered developer tools to accelerate development.

Responsibilities

• Develop and iterate on underwriting prediction models for tabular and sequential data.
• Build and scale feature pipelines and training datasets.
• Prototype modeling ideas, run offline experiments, push best approaches to production with risk controls.
• Productionize models and integrate into batch/real-time decision systems.
• Instrument and monitor model and data health; define retraining workflows.
• Collaborate with Engineering, Risk Analytics, Product, and ML Platform; communicate results clearly.

Skills

Python

Production code

GBDT models

PyTorch

Spark

Kubeflow

MLflow

Code tooling

Education

PhD in a relevant field

Tools

Kubeflow

Airflow

MLflow

Spark

Job description

Affirm is building underwriting ML systems that decide real-time transaction risk and expected value at checkout. You will work with ML engineers, data, and platform teams to move models from idea to production, while monitoring health as user behavior and macro conditions evolve.

You will develop underwriting models, build feature pipelines, prototype ideas, and productionize them with strong risk controls.