Machine Learning Engineer

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’re at 8-figures of annualized revenue, growing quickly, and profitable. We were part of Y Combinator (Summer 2021) and raised a $2M seed round shortly after.

We’re looking for a Machine 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.

• 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.

• 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.

💰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