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Canadian Tire

Canadian retail and services group

Senior Machine Learning/Artificial Intelligence Engineer

Full-TimeUpdated on 10/4/2026Deadline 10/31/26
CA$64k - CA$106k+ Performance incentives
Mid, Senior
Bachelor's, Master's, PhD
Toronto, ON, Canada
HybridFour days per week in-office.

About the job

Requirements
  • A B.S. or M.S., preferably in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline; a PhD is an asset.
  • At least 3 years of experience developing and deploying production machine-learning solutions, owning data, training, evaluation, release, and operational support.
  • Expert-level Python proficiency and the ability to write production-grade machine-learning code rather than notebooks handed off for deployment.
  • At least 4 years of experience querying and analyzing large datasets with tools such as SQL and Spark, with demonstrated depth in PySpark.
  • Demonstrated experience with recommender systems or large-scale ranking, and judgment in selecting the appropriate technique for a problem.
  • A rigorous approach to offline evaluation, including time-respecting train/validation splits, point-in-time-correct feature construction, and personally identifying and fixing leakage.
  • Experience with deep-learning frameworks such as PyTorch or TensorFlow and gradient-boosting libraries such as LightGBM or XGBoost.
  • Production experience with a major cloud machine-learning platform, including custom training jobs, artifact management, and cost control.
  • Experience designing and orchestrating multi-stage analytical pipelines with workflow optimization.
  • Familiarity with agentic artificial-intelligence architectures, large-language-model solutions, and coding agents.
  • Excellent oral and written communication skills to communicate technical and business concepts, and strong presentation skills.
  • Ability to work independently with minimal supervision and navigate and resolve ambiguous problems and situations.
Responsibilities
  • Develop a deep understanding of the retail business, loyalty program, and how personalized offers are composed and delivered across retail banners.
  • Design and deliver offline evaluation frameworks with explicit success criteria, including ranking metrics, held-out AUC, time-respecting splits, and point-in-time correctness, so offline results reliably predict online lift.
  • Research and apply modelling methodology to select and justify an appropriate approach for each business problem, from matrix factorization and two-tower retrieval to learning-to-rank and sequential architectures.
  • Build and optimize large-scale feature engineering in PySpark over hundreds of millions of transaction records, and profile jobs to identify causes of data shuffling.
  • Design and operate the machine-learning platform layer, including a feature store, reproducible training pipelines, and cloud training-job submission, so retraining is routine and cost-effective.
  • Design and implement agentic AI solutions that automate model improvement by extending the in-house harness in which an AI agent proposes changes, scores them against a locked benchmark, and retains only measurably beneficial changes under human review.
  • Lead optimization and orchestration of end-to-end analytical pipelines across multiple data sources and modelling workstreams to ensure scalability and production reliability.
  • Raise the team’s engineering standard through code review, documentation, and mentorship of engineers working adjacent to the model layer.
Desired Qualifications
  • Experience in retail, loyalty programs, personalization, or ad-tech ranking is preferred.
  • Vertex AI experience is preferred; SageMaker or Azure ML experience is also acceptable.
  • Familiarity with BigQuery and cloud-based data structures is an asset.
  • Experience with Apache Airflow at production scale is preferred.
  • Infrastructure-as-code experience with Terraform and familiarity with cloud IAM are assets.

About the company

Canadian Tire is a Canadian group of retail and service businesses serving drivers, households, sports participants, and outdoor consumers. Its banners and operations span automotive products and service, home and hardware, sporting goods, apparel, and financial services. A national dealer and store network connects the company’s brands with communities across Canada. The corporate profile combines a dealer-supported retail network with owned brands and financial operations, so it spans vehicle maintenance, home improvement, sports, recreation, clothing, and consumer credit rather than automotive retail alone. Dealer relationships and distinct retail banners are central to how the group reaches customers.

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