Machine Learning Engineer, Underwriting

About Bree

Chime for Canada, starting with cash advances

Tech description:

- Web application built in React and hosted on Netlify (backend built using Netlify serverless functions and FaunaDB NoSQL database)
- iOS + Android applications (to-build)
- ML models for underwriting (to-build)

Job description:

## About Bree

Bree is a consumer finance platform building faster, simpler, and more affordable financial services for Canadians who often live paycheck to paycheck. We operate in a massive market that’s historically been underserved by traditional financial institutions, and we’re building products that help customers access short-term credit with a transparent, user-first experience.

To date, 800,000+ Canadians have signed up for Bree—and we believe we’re still early. We’re at an exciting intersection of product-market fit, rapid growth, and a clear path to becoming one of the most important fintech companies in Canada.

We’re at 8-figures of annualized revenue, growing quickly, and profitable. We were part of Y Combinator (Summer 2021) and raised a $2M seed round shortly after.

## About the Role

We’re looking for a Machine Learning Engineer to build and scale high-impact, world-class ML systems. You’re passionate about deploying AI solutions, optimizing performance, and driving measurable results. Your work will power critical decisions and shape the future of our technology.

## What You'll Do

* Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference.
* Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies.
* Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques.
* Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation.
* Apply machine learning design patterns to build modular, reusable, and production-ready models.
* Collaborate with data engineers to develop high-performance data pipelines for training and inference.
* Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes.
* Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.

## What You'll Need

* Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch.
* Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques.
* Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows.
* Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL).
* Knowledge of cloud-based ML deployment and infrastructure management.
* Ability to implement real-time and batch inference pipelines efficiently.
* Strong analytical and problem-solving skills to translate business needs into scalable ML solutions.
* Eagerness to work in a fast-paced environment and continuously refine ML processes for efficiency and accuracy.

## **Benefits:**

💰Top of the market compensation for top performers

⚕️Comprehensive health, dental, and vision benefits plan

🖥 $1,500 annual learning & home-office stipend

🧘🏼 $1,000 annual wellness stipend

🍔 Monthly Lunch Stipend

🚗 Commuter Benefits

🚼Paid Parental leave

🏝20 annual PTO days + unlimited sick days

🚀 Quarterly Team Gatherings

☕ In Office Amenities