Senior ML/AI Engineer

Canadian Tire; Toronto; Hybrid; CAD 64,000 - 106,000; Full time

Benefits offered by this job

Profit Sharing

Product Discounts

Career Growth Opportunities

Mental Health Benefits

Job summary

Canadian Tire Corporation in Toronto, Ontario is seeking a Senior ML/AI Engineer to advance personalization, recommendations, and ML platform infrastructure at scale. The role focuses on building offline evaluation frameworks, applying state-of-the-art modelling, and delivering scalable data pipelines.

You will work with Python, PySpark, and cloud ML platforms, shaping end-to-end analytical workstreams while mentoring teammates and raising engineering standards.

Qualifications

• 3+ years of production ML experience in retail, loyalty, personalization or ad-tech ranking
• Expert-level Python for production ML code, not notebooks
• 4+ years SQL and PySpark with large datasets
• Experience with recommender systems or large-scale ranking
• Strong offline evaluation discipline with time-respecting splits
• Experience with deep learning frameworks (PyTorch, TensorFlow) and GBMs (LightGBM/XGBoost)

Responsibilities

• Develop understanding of Retail, Loyalty, personalized offers delivery
• Design offline evaluation frameworks with ranking metrics and hold-out tests
• Research modelling approaches for business problems and justify choices
• Build large-scale feature engineering in PySpark
• Design and operate ML platform components including feature store and training pipelines
• Implement agentic AI solutions and evaluate changes against benchmarks
• Lead end-to-end analytic pipelines across data sources and workstreams
• Mentor engineers and improve code quality through reviews and documentation

Skills

Python

PySpark

SQL

Data analysis

Cloud platforms

Education

B.S. or M.S. in CS/Stats/Math/Engineering

Tools

Apache Airflow

BigQuery

Terraform

Cloud ML platforms (Vertex AI)

Job description

Canadian Tire Corporation is seeking a Senior ML/AI Engineer in Toronto, Ontario to work on personalization, recommendation systems, and ML platform infrastructure at scale.

What You’ll Do

• Develop a deep understanding of our Retail business, Loyalty program, and how personalized offers are composed and delivered across our retail banners.
• Design and deliver offline evaluation frameworks with explicit success criteria — ranking metrics, held-out AUC, time-respecting splits, and point-in-time correctness — so that an offline win reliably predicts online lift.
• Research and apply modelling methodology to select (and justify) the right approach for a given 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, with the profiling skills to know why a job is shuffling.
• Design and operate our ML platform layer — feature store, reproducible training pipelines, and cloud training job submission — so retraining is cheap and routine rather than a project.
• Design and implement agentic AI solutions that automate model improvement — extending our in-house harness where an AI agent proposes changes, scores them against a locked benchmark, and keeps only what measurably helps, 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 engineering standard of the team through code review, documentation, and mentorship of engineers working adjacent to the model layer.

What You Bring

• B.S or M.S, preferably in Computer Science/Statistics/Math/Engineering or a related quantitative discipline. PhD an asset.
• 3+ years experience developing and deploying machine learning solutions in production — owning data, training, evaluation, release, and operational support. Experience in retail, loyalty programs, personalization, or ad-tech ranking preferred.
• Expert-level Python, writing production-grade ML code rather than notebooks handed off for deployment.
• 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 the judgment to know which technique a problem actually requires.
• Rigorous approach to offline evaluation — including train/validation splits that respect time, point-in-time correct feature construction, and leakage you have personally found and fixed.
• Experience with deep learning frameworks (PyTorch, TensorFlow) and gradient boosting libraries such as LightGBM or XGBoost.
• Production experience with a major cloud ML platform — Vertex AI preferred; SageMaker or Azure ML acceptable — including custom training jobs, artifact management, and cost control. Familiarity with BigQuery and cloud-based data structures is an asset.
• Experience designing and orchestrating multi-stage analytical pipelines with workflow optimization, preferably Apache Airflow at production scale.
• Familiarity with agentic AI architectures, LLM-based solutions, and working fluently with coding agents is a strong asset. Infrastructure-as-code experience (Terraform) and cloud IAM familiarity is an asset.
• Excellent oral and written communication skills, with the ability to communicate both technical and business concepts, as well as strong presentation skills.
• Demonstrated ability to work independently with minimal supervision, effectively navigating and resolving ambiguous problems and situations.

Location

Toronto, Ontario (Hybrid: In-office 4 days a week)

Compensation & Benefits

Broadband Salary Range: $64,000 - $106,000. Typical hiring range: $64,000 - $85,000. Comprehensive benefits and retirement programs, performance incentives, continuing education programs, $5,000/year mental health benefits, Triangle Learning Academy, Canadian Tire Profit Sharing, career growth opportunities, and product discounts.

B.S. or M.S. in Computer Science, Statistics, Math, Engineering or related quantitative discipline; 3+ years production ML experience; expert-level Python; 4+ years SQL and PySpark; recommender systems or large-scale ranking experience; deep learning frameworks (PyTorch, TensorFlow); cloud ML platform experience (Vertex AI preferred)