Machine Learning Engineer II, Underwriting ML

What you'll do

• Develop and iterate on underwriting prediction models for tabular and sequential data.
• Build and scale feature pipelines and training datasets from proprietary and third-party signals.
• Prototype new modeling ideas and run offline experiments to drive high-performing approaches into production.
• Integrate models into batch and real-time decision systems while improving reliability and latency.
• Instrument and monitor model and data health to define retraining and backtesting workflows.
• Write production-quality, well-tested, and extensible code to solve complex business scenarios.
• Perform code reviews and debug existing code within a large software codebase.

What we're looking for

• You must have at least 2 years of experience as a machine learning engineer or a PhD in a relevant field.
• You must have a Bachelor’s degree in a related field or equivalent practical experience.
• You must have strong Python skills and experience writing production-quality code.
• You must have experience building and evaluating models for classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost).
• You must have experience with a deep learning framework (PyTorch preferred).
• You must have experience with distributed data processing or parallel compute frameworks (Spark preferred).
• You must have experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring.
• You must be proficient in using AI-powered developer tools to accelerate iteration and debugging.