Machine Learning Engineer

About the role

Bree is a consumer finance platform building faster, simpler and more affordable financial services for Canadians, backed by Y Combinator and a $2M seed round. It is looking for a Machine Learning Engineer to build and scale high-impact ML systems, deploy AI solutions and optimize performance.

Key responsibilities

• Design, develop and deploy end-to-end machine learning pipelines, covering training, validation and inference.
• Implement MLOps best practices: CI/CD for ML models, model versioning, monitoring and retraining strategies.
• Optimize models through feature engineering, hyperparameter tuning and scalable inference.
• Work with structured and unstructured data using Pandas, NumPy and SQL.
• Apply ML design patterns to build modular, reusable, production-ready models.
• Collaborate with data engineers on high-performance data pipelines.
• Deploy and manage models on AWS, GCP or Azure with Docker and Kubernetes.
• Maintain model performance with continuous monitoring, bias detection and explainability.

Required profile

• Strong understanding of supervised and unsupervised learning algorithms.
• Experience with MLOps tools such as MLflow, Kubeflow or SageMaker.
• Hands-on experience with Pandas, NumPy, SQL and NoSQL.
• Ability to implement real-time and batch inference pipelines.
• Strong analytical and problem-solving skills and eagerness to work in a fast-paced environment.

Required skills

• Python, Scikit-learn, LightGBM and PyTorch
• Pandas, NumPy, SQL and NoSQL
• MLflow, Kubeflow or SageMaker
• AWS, GCP or Azure, Docker and Kubernetes

What we offer

• Competitive compensation for top performers
• Health, dental and vision coverage
• $1,500 setup bonus and $500/year learning and home-office stipend
• $1,500 annual wellness stipend
• Free lunch in the office and $250/month stipend for working from home days
• Fully remote in the winter
• $150/month commuter benefits and parental leave
• 20 annual PTO days plus unlimited sick days and an annual retreat