Machine Learning Engineer, Underwriting

Summary

Bree is a consumer finance platform focused on providing affordable financial services for Canadians. They are seeking a Machine Learning Engineer to design, develop, and deploy machine learning systems that will drive critical decisions and enhance their technology.

Responsibilities

• Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference
• Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies
• Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques
• Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation
• Apply machine learning design patterns to build modular, reusable, and production-ready models
• Collaborate with data engineers to develop high-performance data pipelines for training and inference
• Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes
• Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques

Skills

• Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch
• Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques
• Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows
• Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL)
• Knowledge of cloud-based ML deployment and infrastructure management
• Ability to implement real-time and batch inference pipelines efficiently
• Strong analytical and problem-solving skills to translate business needs into scalable ML solutions
• Eagerness to work in a fast-paced environment and continuously refine ML processes for efficiency and accuracy

Qualifications

Must Haves

• Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch
• Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques
• Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows
• Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL)
• Knowledge of cloud-based ML deployment and infrastructure management
• Ability to implement real-time and batch inference pipelines efficiently
• Strong analytical and problem-solving skills to translate business needs into scalable ML solutions
• Eagerness to work in a fast-paced environment and continuously refine ML processes for efficiency and accuracy

Benefits

• Top of the market compensation for top performers
• Comprehensive health, dental, and vision benefits plan
• $1,500 annual learning & home-office stipend
• $1,000 annual wellness stipend
• Monthly Lunch Stipend
• Commuter Benefits
• Paid Parental leave
• 20 annual PTO days + unlimited sick days
• Quarterly Team Gatherings
• In Office Amenities