Senior ML/AI Engineer

canadiantirecorporation; Toronto; Hybrid; CAD 64,000 - 106,000; Full time

Benefits offered by this job

Comprehensive benefits and retirement

Performance incentives

Continuing Education Programs

Career growth opportunities

Product discounts

Job summary

Canadian Tire Corporation, Limited in Toronto, Ontario is seeking a seasoned ML Engineer to design, deploy, and operate production ML solutions across retail loyalty and personalization initiatives. Hybrid work arrangement with in-office 4 days per week.

You will own data, training, evaluation, release, and operational support, building scalable feature pipelines in PySpark, and collaborating with teams to raise engineering standards while delivering measurable online lift.

Qualifications

• 3+ years of production ML development experience in retail or related fields.
• Expert-level Python and production-grade ML coding skills.
• Strong SQL and PySpark experience with large datasets.
• Experience with recommender systems or large-scale ranking is a plus.
• Deep learning frameworks and cloud ML platforms familiarity.

Responsibilities

• Develop and deploy ML solutions in production, owning data, training, evaluation, release, and operational support.
• Design offline evaluation frameworks with ranking metrics and time-respecting splits.
• Research modelling methods and justify approach for business problems.
• Build large-scale feature engineering in PySpark over hundreds of millions of records.
• Lead end-to-end analytical pipelines for scalability and reliability.

Skills

Python

SQL

Spark

PyTorch

TensorFlow

BigQuery

Education

Bachelor's or Master's in CS/Statistics/Math/Engineering

PhD an asset

Tools

Vertex AI

AWS SageMaker

Azure ML

Airflow

Terraform

BigQuery

Job description

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)

We’re always looking for great talent! In addition to competitive pay, we offer:

• Comprehensive benefits and retirement programs
• Performance incentives, Continuing Education Programs
• Other perks to support your well-being
• Career growth opportunities and product discounts

Broadband Salary Range: $64,000 - $106,000.

Our typical hiring range is between $64,000 and $85,000. Salary decisions are also dependent on other factors such as your experience, industry benchmarks, internal equity and other role-specific requirements. For critical roles, the compensation offering will be reviewed to ensure alignment with market rate and conditions and the unique value you bring to the role.

We may use artificial intelligence tools as part of our recruitment process to assist in the initial screening of resumes. All hiring decisions, including candidate evaluation, selection, and disposition, are made by human recruiters.

About Us

Canadian Tire Corporation, Limited ("CTC") is one of Canada’s most admired and trusted companies. With more than 90 Owned Brands, over 1,600 retail locations, financial services, exemplary e-commerce capabilities, and exciting market-leading merchandising strategies. We dream big and work as one to innovate with purpose for our customers at every level of our business, investing in new technologies an

This posting represents an existing vacancy within our organization.