Machine Learning Engineer Lead
$125,000–$169,100 year
RemoteOntario, California, United States
Job Summary
Design, build, and deploy enterprise-wide machine learning solutions across the full lifecycle, including model development, feature engineering, MLOps, and continuous monitoring. Architect scalable AI systems integrated into production workflows using Azure ML, Databricks, and MLflow, while developing reusable pipelines and automated deployment processes. Implement production-grade Python code and governance standards to ensure reliable model performance, drift detection, and operational health. This full-time, permanent role reports to the Manager, Data Science & AI and requires on-site presence for events and business demands. Candidates must be located in British Columbia or Ontario.
Required Qualifications
- 10+ years of experience in Machine Learning Engineering, Data Science, Applied AI, Software Engineering, or related disciplines
- Bachelor's or Master's degree in Computer Science, Software Engineering, Mathematics, Statistics, or a related quantitative field
- Strong hands-on experience building and deploying cloud-based applications and machine learning services
- Strong proficiency in Python and SQL, with solid software engineering fundamentals including data structures, algorithms, and object-oriented design
- Hands-on experience with industry-standard machine learning and deep learning frameworks such as PyTorch, TensorFlow, and Scikit-learn
- Proven experience productionizing machine learning models and operating scalable, reliable ML systems in enterprise environments
- Hands-on experience with Azure Machine Learning, Databricks, MLflow, CI/CD pipelines, model lifecycle management, monitoring, and deployment automation
- Strong understanding of API design and service integration, machine learning algorithms, statistical modeling, feature engineering, and model evaluation techniques
- Proven ability to take solutions from prototype to production
- Must be available for on-site events and business demands
Desired Qualifications
- Exposure to machine learning use cases such as churn prediction, forecasting, predictive modeling, member or customer personalization, recommendation systems, marketing optimization, and experimentation frameworks such as A/B testing
- Familiarity with advanced machine learning techniques including anomaly detection, graph neural networks, optimization methods, representation learning, causal inference, and Generative AI workflows
- Experience integrating AI services into automation platforms such as UiPath or Power Automate and familiarity with AWS, GCP, Power BI, or Tableau
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