Machine Learning Engineer, Underwriting

Role: Machine Learning Engineer, Underwriting

Bree is seeking a Machine Learning Engineer to build and scale high-impact, production-grade ML systems that power critical underwriting decisions.

What you’ll do

• Design, develop, and deploy end-to-end machine learning pipelines for training, validation, and inference.
• Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies.
• Improve model performance through feature engineering, hyperparameter tuning, and scalable inference techniques.
• Work with structured and unstructured data, using Pandas, NumPy, and SQL for data manipulation.
• Apply machine learning design patterns to create modular, reusable, production-ready models.
• Collaborate with data engineers to deliver high-performance data pipelines for training and inference.
• Deploy and manage models on cloud platforms (AWS, GCP, Azure) using Docker and Kubernetes.
• Maintain and improve model performance with continuous monitoring, bias detection, and explainability.

What you’ll need

• Proficiency in Python and familiarity with ML libraries such as scikit-learn, LightGBM, and PyTorch.
• Strong knowledge of machine learning algorithms (both supervised and unsupervised learning).
• Experience with MLOps tooling such as MLflow, Kubeflow, or SageMaker.
• Hands-on experience with Pandas, NumPy, and SQL (and exposure to NoSQL).
• Knowledge of cloud ML deployment and infrastructure management.
• Ability to implement real-time and batch inference pipelines.
• Strong analytical/problem-solving skills to translate business needs into scalable ML.

Nice to have / environment

• Comfortable working in a fast-paced environment and continuously improving ML efficiency and accuracy.