Senior ML Engineer - Personalization & Retail AI

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

Benefits offered by this job

Benefits & retirement

Performance incentives

Continuing education

Well-being perks

Career growth & discounts

Broadband

Job summary

Canadian Tire Corporation in Toronto, Ontario, is hiring a senior ML Engineer/Data Scientist to design and deploy recommender systems and offline evaluation frameworks. You will build large-scale feature engineering in PySpark on hundreds of millions of transactions and operate our ML platform layer for reliable retraining and cloud training jobs.

You will lead end-to-end analytical pipelines across multiple data sources, applying state-of-the-art modelling methods and agentic AI concepts.

Qualifications

• BS or MS in Computer Science/Statistics/Math/Engineering or related quantitative discipline.
• PhD an asset.
• 3+ years producing ML in production: data, training, evaluation, release, and ops.

Responsibilities

• Develop understanding of Retail, Loyalty, and personalized offers.
• Design offline evaluation frameworks with explicit success criteria.
• Research modelling methods and justify approaches.
• Build and optimize large-scale PySpark feature engineering.
• Operate ML platform layer: feature store, training pipelines, cloud training jobs.
• Design agentic AI solutions to automate model improvement under human review.
• Lead end-to-end analytical pipelines across data sources for scalability and reliability.
• Mentor engineers and improve code quality through reviews and docs.

Skills

Python

SQL

Spark

Machine learning

Deep learning

PyTorch

TensorFlow

BigQuery

Airflow

Vertex AI

Recommender systems

Time-aware evaluation

Education

BS/MS in CS/Statistics/Math/Engineering

PhD asset

Tools

PySpark

TensorFlow

PyTorch

LightGBM

XGBoost

Airflow

Terraform

BigQuery

Job description

Canadian Tire Corporation in Toronto, Ontario, is hiring a senior ML Engineer/Data Scientist to design and deploy recommender systems and offline evaluation frameworks. You will build large-scale feature engineering in PySpark on hundreds of millions of transactions and operate our ML platform layer for reliable retraining and cloud training jobs.

You will lead end-to-end analytical pipelines across multiple data sources, applying state-of-the-art modelling methods and agentic AI concepts.