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.
More than half a million Canadians have already signed up with Bree, and we believe we are just scratching the surface. We are in an exciting place where we have product market fit, explosive growth, and a clear path to becoming one of the most important FinTechs in Canada.
We have $5M ARR per full-time engineer, growing at a double-digit monthly rate, profitable, and have had 0 voluntary employee churn. We were part of Y Combinator in 2021 and raised a $2M seed round shortly after.
About the Role
Bree is on a mission to build the best AI native engineering team. Our ideal Machine Learning Engineer has a deep 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.
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.
Own 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.
Mentor junior team members and contribute to a culture of innovation, sharing insights from competitive ML experiences (e.g., Kaggle competitions, research publications) to elevate the team's capabilities.
What You'll Need
Proven expertise in building and deploying production ML systems and handling imbalanced datasets in high-stakes domains like finance or e-commerce.
Mastery 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.
A passion for FinTech and consumer impact, with a bias toward action and a history of shipping ML features that drive measurable results, such as improved accuracy in credit decisions or reduced fraud losses.
Benefits
Top of the market compensation for top performers
$1,500 annual learning stipend
$1,000 annual wellness stipend
$250 monthly lunch stipend
Comprehensive insurance coverage
2 annual company retreats
Parental leave
Unlimited PTO
