Machine Learning Engineer, Underwriting
At a glance
Mid-level Machine Learning Engineering role at Bree. Remote, Canada · full-time · CA$180,000–CA$250,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and launch full ML workflows, covering model training, validation and inference
- Set up model release practices, version control, monitoring and retraining workflows
- Improve models through feature design, parameter tuning and scalable inference methods
- Work with data engineers on fast data pipelines for model training and predictions
- Deploy models to cloud environments and track performance, bias and explainability
What you bring
- You’re proficient in Python and familiar with Scikit-learn, LightGBM or PyTorch
- You understand supervised and unsupervised methods and have applied machine learning algorithms
- You’ve used MLflow, Kubeflow or SageMaker to track experiments and automate workflows
- You can manage data with Pandas, NumPy and SQL or NoSQL databases
Who this fits
This mid-level role suits an engineer who wants to build and operate production machine learning systems, including both batch and real-time inference. It is remote in Canada.
From the employer
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 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 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