# Machine Learning Engineer, Underwriting at Bree

Machine Learning Engineer, Underwriting at Bree in Remote. Remote, full-time, paying $180k–$250k. Posted May 11, 2026.

- Company: Bree
- Location: Remote
- Workplace: Remote
- Job type: Full-time
- Pay: $180k–$250k
- Posted: 2026-05-11
- Apply: https://jobs.ashbyhq.com/bree/f33dd42a-6a68-495d-baa6-2830ca9e5834/application
- Source: https://jobmagic.ai/jobs/machine-learning-engineer-underwriting-bree-3rzfkb

## 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 were part of Y Combinator (Summer 2021) and raised a $2M seed round shortly after.

### About the Role

We’re looking for aMachine 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 one-time setup + $500/year annual learning & home-office stipend
🧘🏼 $1,500 annual wellness stipend
🥗 Free lunch provided in office daily
❄️ Remote-first during the winter
🚗 Commuter Benefits
🚼Paid Parental leave
🏝20 annual PTO days + unlimited sick days
🚀 Quarterly Team Gatherings
☕ In Office Amenities