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Synced from Ashby · Jul 15

Machine Learning Engineering, Intern

BreeTorontoPosted Jul 15, 2026
Machine Learning EngineerHybridIntern
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Mirrored from Bree's own Ashby careers system · refreshed hourly

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Jul 15
Posted
Ashby
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Job 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.

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

View original posting on Ashby

What applying to Bree usually looks like

Based on publicly available information, candidates applying through Ashby can generally expect a structured and relatively streamlined process, as this ATS is commonly used by growth-stage technology companies, including Bree, to manage applications efficiently. Applicants typically submit a resume and may be asked to complete optional or required screening questions. The process may include multiple stages, such as an initial recruiter conversation, hiring manager discussions, and role-specific evaluations like technical exercises, portfolio reviews, or case studies depending on the position. Communication is often handled through automated status updates within the platform, though response times vary by employer and role. Candidates may also encounter self-scheduling tools for interviews. As with most ATS-driven processes, preparation should focus on clearly demonstrating relevant skills and experience aligned with the specific job posting.

Based on publicly available information. LandEarly does not verify interview process details.

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Machine Learning Engineering, Intern
Bree · Toronto
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