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Canadian Tire

Sells automotive, home, sporting and apparel products

Senior Machine Learning/Artificial Intelligence Engineer

Full-TimeDeadline 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.
AI & Machine LearningApplied Machine Learning

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 retail and services group serving households, drivers and people taking part in sport and outdoor activities. Its stores and online shops sell automotive supplies, tools, home and garden products, sporting goods and clothing, with vehicle maintenance available through its automotive service network. The wider group includes banners such as SportChek, Mark's and PartSource, alongside financial services and commercial real estate operations that support the retail network. It combines national retail brands with local dealers and franchise operators to reach customers across Canada. Founded in 1922, Canadian Tire is headquartered in Toronto, Ontario, Canada.

Company Size

10,001+

Company Stage

N/A

Total Funding

N/A

Headquarters

Toronto, Canada

Founded

1922

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Simplify's Take

What believers are saying

  • Mental health coverage has a stated allowance: eligible employees and their families receive $5,000 annually under enhanced flex benefits.
  • Learning has dedicated platforms: corporate training includes Triangle Learning Academy and the technology-focused Agile Academy.
  • Distribution profit-sharing awards support savings: eligible employees choose cash, TFSA, DPSP or group RRSP payments.

What critics are saying

  • Hybrid work includes campus attendance: corporate teams meet in person for key projects and collaboration.
  • Order-support work carries daily incident duties: the Toronto superuser role requires error management and exception analysis.
  • Procurement leaders face external shocks: the director role must mitigate tariffs, commodity movements and geopolitical sourcing risks.

What makes Canadian Tire unique

  • The portfolio includes proprietary products: its current corporate vacancy describes more than 90 owned brands.
  • Loyalty spans the shopping journey: Triangle Rewards lets customers collect and redeem Canadian Tire Money.
  • Distribution uses automation: its centres combine automated material handling with dedicated maintenance operations and safety systems.

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