Bree
Machine Learning Engineer, Underwriting
About this role
Bree, a rapidly growing Canadian fintech serving underserved consumers, seeks a Machine Learning Engineer to design and deploy production-grade ML systems for underwriting decisions. You'll build end-to-end pipelines, implement MLOps practices, and optimize models across the full lifecycle while collaborating with data engineering teams.
What you'll do
- Design and deploy end-to-end ML pipelines with focus on training, validation, and inference efficiency
- Implement MLOps best practices including CI/CD, model versioning, monitoring, and automated retraining
- Optimize models through feature engineering, hyperparameter tuning, and scalable inference techniques
- Deploy and manage models on cloud platforms (AWS, GCP, Azure) using Docker and Kubernetes
- Maintain model performance through continuous monitoring, bias detection, and explainability
- Collaborate with data engineers on high-performance data pipelines for training and inference
What they're looking for
- Python programming
- ML frameworks (Scikit-learn, LightGBM, PyTorch)
- MLOps tools (MLflow, Kubeflow, SageMaker)
- Data manipulation (Pandas, NumPy, SQL, NoSQL)
- Cloud-based ML deployment and infrastructure
- Real-time and batch inference pipeline development
- Machine learning algorithms (supervised and unsupervised)
- Docker and Kubernetes containerization
Benefits
- Competitive compensation for top performers
- Comprehensive health, dental, and vision coverage
- $1,500 annual learning and home-office stipend
- $1,000 annual wellness stipend
- 20 PTO days plus unlimited sick days and paid parental leave
- Monthly lunch stipend, commuter benefits, and quarterly team gatherings
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Bree
Bree is a Canadian fintech that serves underserved consumers through advanced machine learning and data systems. The company is hiring Machine Learning Engineers to design and deploy production ML systems for underwriting decisions, build end-to-end pipelines, and implement MLOps practices.
View all jobs at BreeLikely interview questions
- Can you walk us through how you've built and deployed an end-to-end ML pipeline from training to production inference? What tools and frameworks did you use?
- Tell us about your experience with MLOps practices. How have you implemented model versioning, monitoring, and retraining strategies in a production environment?