Senior Machine Learning Engineer

Vancity • Vancouver, BC

2d • CA$113,100 - CA$153,000 • Remote

About The Position

As a Senior Machine Learning Engineer, you will join our Data Science & AI Pod, focused on designing, building, and deploying enterprise-wide AI and machine learning solutions that support business decision-making, automation, and operational efficiency. This role is highly hands-on and best suited for an engineer who can design, build, deploy, and operationalize machine learning models in enterprise production environments. You will work across the full ML lifecycle, including model development, feature engineering, MLOps, deployment automation, monitoring, and continuous improvement of machine learning systems. Success in this role is measured by scalable, reliable, and production-ready machine learning solutions—not proof-of-concepts or experimentation alone.

This is a Full-time, Permanent role and will report directly to the Manager, Data Science & AI. This position is remote and open to candidates located in British Columbia or Ontario. While this position provides a remote work arrangement, you will be expected to be on-site for events and business demands.

Requirements

• 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

Nice To Haves

• 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

Responsibilities

• Applying Data Science and Machine Learning best practices to develop robust models and support data-driven decision-making across business domains
• Applying machine learning and data science techniques such as forecasting, predictive modeling, classification, regression, recommendation, and optimization to solve business problems
• Conducting experiments and evaluating models using appropriate statistical, technical, and business performance metrics
• Architecting, building, deploying, and maintaining scalable machine learning models and AI solutions integrated into enterprise systems, applications, and operational workflows
• Designing and implementing end-to-end ML workflows, including data preparation, feature engineering, model training, validation, deployment, optimization, and continuous monitoring in a high-scale production environment
• Developing reusable machine learning components, feature pipelines, and model-serving frameworks to support multiple use cases and teams
• Designing and implementing production-grade MLOps solutions using Azure ML, Databricks, MLflow, and related cloud technologies
• Building and maintaining automated ML pipelines, feature engineering workflows, feature store patterns, and deployment processes for training, testing, monitoring, and retraining machine learning models
• Implementing standards and best practices for model versioning, lifecycle management, governance, deployment automation, model performance monitoring, drift detection, data quality, operational health, and retraining triggers
• Developing production-quality Python code, APIs, automation workflows, and machine learning services to integrate ML capabilities into business applications and processes

Benefits

• Competitive rewards and benefits
• Flexible benefit packages that can be tailored annually to meet evolving needs
• 3-4 weeks of vacation per year, with additional days earned over time
• 2 extra stat holidays, plus care days for personal or family illness
• Health and dental benefits begin on your hire date, with three levels of coverage to choose from
• Defined Benefit Pension

What This Job Offers

• Job Type: Full-time
• Career Level: Senior