Machine Learning Engineer, Underwriting

Job description

## About Bree Bree is a consumer finance platform building faster, simpler, and more affordable financial services for Canadians who often live paycheck to paycheck. We operate in a massive market that’s historically been underserved by traditional financial institutions, and we’re building products that help customers access short-term credit with a transparent, user-first experience. To date, 800,000+ Canadians have signed up for Bree—and we believe we’re still early. We’re at an exciting intersection of product-market fit, rapid growth, and a clear path to becoming one of the most important fintech companies in Canada. We were part of Y Combinator (Summer 2021) and raised a $2M seed round shortly after. ## About the Role We’re looking for aMachine Learning Engineer to build and scale high-impact, world-class ML systems. You’re passionate about deploying AI solutions, optimizing performance, and driving measurable results. Your work will power critical decisions and shape the future of our technology. ## What You'll Do - 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. ## What You'll Need - 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