Machine Learning Engineering, Intern

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Description

About Bree Bree is a consumer finance platform that brings better, faster, and cheaper financial services to over half the Canadian population who live paycheck to paycheck. We operate in a huge, but overlooked market in a country with the least amount of financial technology innovation in the developed world. Our first act is to become the cheapest and best provider of short-term credit to the 20 million people in Canada who live paycheck to paycheck. 500,000+ Canadians have already signed up with Bree and we believe we are just scratching the surface. We are at an exciting intersection of product market fit, explosive growth, and a clear path to becoming one of the most important FinTechs in Canada. We are at 8-figures of annualized revenue, growing rapidly, profitable, and have had zero voluntary employee churn. We were part of Y Combinator's Summer 2021 batch and raised a $2M seed round shortly after. About the Role Our ideal Machine Learning Engineer has a good understanding of modern ML systems and deploying models at scale in production environments. You'll enjoy leveraging AI tools to iterate quickly on models, experiment with cutting-edge techniques, and deliver high-impact solutions efficiently and reliably. Read more about AI native engineering teams here . We are open to an 8-month co-op term. What You'll Do Design, train, and deploy scalable machine learning models for critical FinTech applications, including credit risk assessment, fraud detection, and personalized financial recommendations, using frameworks like PyTorch and LightGBM. Architect ML pipelines integrating with backend systems to process high-throughput data streams with low-latency inference for real-time decision-making. Leverage AI tools to automate experimentation, hyperparameter tuning, and test-driven ML development, accelerating the delivery of robust, production-ready models. Support the full ML lifecycle, including feature engineering, model evaluation, A/B testing, monitoring for

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