Machine Learning Engineer II

What you’ll do

- You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data

- You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed.

- You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.

- You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness.

- You will instrument and monitor model and data health, and help define retraining/backtesting workflows

- You will 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.

What we look for

- You have 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.

- You have 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.

- You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.

- Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.

- You have strong verbal and written communication skills that support effective collaboration with our global engineering team.

- This position requires either equivalent practical experience or a Bachelor’s degree in a related field

Skills Required
2+ years as a machine learning engineer or a PhD in a relevant field
Bachelor's degree or equivalent practical experience
Strong Python skills and experience writing production-quality code
Experience building and evaluating classification models (e.g., gradient-boosted decision trees like LightGBM, XGBoost, CatBoost)
Experience with a deep learning framework (PyTorch preferred)
Experience 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 (Kubeflow, Airflow, MLflow, or equivalent)
Experience integrating models into batch and/or real-time decision systems and improving reliability, latency, and operational robustness
Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar)
Ability to write clear, well-tested, extensible code; navigate and debug large codebases; participate in code reviews
Strong verbal and written communication skills and experience collaborating across cross-functional teams