Machine Learning Engineering, Intern

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 https://blog.nilenso.com/blog/2025/05/29/ai-assisted-coding/. 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 drift, and seamless scaling to support explosive user growth while ensuring compliance with financial regulations. - Experiment with advanced techniques in deep learning and reinforcement learning to push the boundaries of what's possible in consumer finance. What You'll Need - Professional experience in building and deploying production ML systems and handling imbalanced datasets in high-stakes domains like finance or e-commerce. - Good understanding of traditional ML systems and modern deep learning/reinforcement learning architectures, with a track record of applying them to real-world problems. - Competitive ML experience (e.g., top rankings in Kaggle, NeurIPS challenges, or open-source contributions) is a bonus, demonstrating your ability to innovate under constraints and deliver high-performance models. - Architectural thinking to solve ambiguous, data-driven problems in fast-paced settings, with experience scaling ML systems under explosive growth while maintaining accuracy, fairness, and explainability. - Exceptional collaboration and communication skills, including the ability to explain complex ML concepts to non-technical stakeholders, thriving in low-churn teams focused on excellence, ethical AI, and long-term impact. Benefits - Compensation: $50-$65/hour, based on experience and interview performance - Offer Matching: We're open to matching competing offers - Perks: $250 monthly lunch stipend, bi-annual company retreat - Impact: Push to prod, with 10x the ownership and impact of typical roles - Growth: Mentorship programs and career training sessions - Path to Full-Time: Strong conversion opportunities for high performers