Machine Learning Engineering, Intern

Job summary

Design, train, and deploy scalable machine learning models for credit risk, fraud detection, and personalized financial recommendations. Architect ML pipelines and leverage AI tools to automate experimentation and support the full ML lifecycle.

Job details

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 800,000 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. About the Role We’re looking for a Machine Learning Engineering Intern to work alongside our ML, data, and infrastructure teams. You’ll contribute to real modelling and data problems, learn how production ML systems are evaluated and monitored, and use AI tools thoughtfully to move from experimentation to reliable implementation. This is an 8-month co-op term. At Bree, co-ops are full members of the Engineering team. You’ll work on the same customer and business problems as full-time engineers, ship real production work, and take part in design discussions, code reviews, testing, and releases. We pair that responsibility with close mentorship, clear context, and projects scoped for you to make a meaningful impact from day one. What You'll Do Help prepare, explore, and validate data used in models and analytical workflows. Support training and evaluation of models used for areas such as credit risk, fraud detection, and customer experience. Build scripts, tools, and tests that make experimentation and model evaluation more repeatable. Learn how model performance is monitored in production, including data quality, drift, and operational reliability. Explore new approaches with mentorship, then clearly document results, tradeoffs, and next steps. What You'll Need Currently enrolled in a Computer Science, Statistics, Engineering, Data Science, or related post-secondary programme, and available for the full 8-month term. Strong Python foundations, plus experience working with data through coursework or projects. Familiarity with SQL, pandas, or similar tools is helpful. Foundational knowledge of statistics and machine learning concepts, with coursework, research, personal projects, or competitions you can discuss. Interest in tools such as PyTorch, LightGBM, or modern LLM workflows. Production ML experience is not required. Curiosity, rigour, and strong communication. You enjoy investigating ambiguous problems, checking your assumptions, and learning from feedback. Benefits Compensation: $50-$70/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

What you’ll do

Design, train, and deploy scalable machine learning models for credit risk, fraud detection, and personalized financial recommendations. Architect ML pipelines and leverage AI tools to automate experimentation and support the full ML lifecycle.

Requirements

Requires professional experience building production ML systems and handling imbalanced datasets in high-stakes domains like finance. Candidates should possess a strong understanding of deep learning architectures and the ability to communicate complex concepts to non-technical stakeholders.

Benefits

• Monthly Lunch Stipend • Bi-annual Company Retreat • Mentorship Programs • Career Training Sessions • Conversion Opportunities to Full-Time

Listed skills

• Collaboration · Preferred
• Machine learning · Preferred
• Communication · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine Learning
• PyTorch
• LightGBM
• Deep Learning
• Reinforcement Learning
• Feature Engineering
• A/B Testing
• Model Deployment
• Credit Risk Assessment
• Fraud Detection
• ML Pipelines
• Data Processing
• Hyperparameter Tuning
• Architectural Thinking
• Collaboration
• Communication

Job areas

• Technology
• Data & Analytics
• Software
• Engineering
• Finance & Accounting