ML Engineer II, Underwriting — Remote

Affirm; Denver (CO); Remote; USD 146,000 - 206,000; Full time

Benefits offered by this job

Health care coverage

Flexible Spending Wallets

Time off

ESPP

Job summary

Affirm is reinventing credit with a remote-first approach, offering real-time underwriting decisions built from diverse signals. You’ll develop predictive underwriting models, scale feature pipelines, and productionize models while ensuring reliability and monitoring.

You’ll work with ML engineers, data teams, and cross-functional partners to drive experiments from idea to deployment. The role emphasizes Python proficiency, production-quality code, and experience with gradient-boosted trees,

Qualifications

• 2+ years in ML engineering or a related PhD.
• Strong Python and production-quality coding skills.
• Experience with classification models and gradient boosting.
• Exposure to deep learning frameworks (PyTorch).
• Experience with distributed data processing (Spark).
• Familiar with ML lifecycle tools and model monitoring.

Responsibilities

• Develop and iterate underwriting prediction models for tabular and sequential data.
• Build feature pipelines and training datasets from multiple signals.
• Prototype ideas, run offline experiments, and move best approaches to production.
• Productionize models in batch or real-time systems with reliability and latency focus.
• Instrument model/data health and define retraining workflows.
• Collaborate with Engineering, Risk Analytics, Product, and ML Platform.

Skills

Python

Production-grade code

Classification models

LightGBM/XGBoost/CatBoost

PyTorch

Spark

ML lifecycle tooling

AI developer tools

System integration

Education

Bachelor’s degree in a related field

Tools

LightGBM

XGBoost

CatBoost

PyTorch

Spark

Kubeflow

Airflow

MLflow

Claude Code

Job description

Affirm is reinventing credit with a remote-first approach, offering real-time underwriting decisions built from diverse signals. You’ll develop predictive underwriting models, scale feature pipelines, and productionize models while ensuring reliability and monitoring.

You’ll work with ML engineers, data teams, and cross-functional partners to drive experiments from idea to deployment. The role emphasizes Python proficiency, production-quality code, and experience with gradient-boosted trees,