Senior ML/AI Engineer

Canadian Tire Financial Services; Toronto; Híbrido; CAD 64.000 - 106.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Comprehensive benefits and retirement

Career growth opportunities

Product discounts

Descripción de la vacante

Canadian Tire Financial Services in Toronto, Ontario is seeking a Senior ML/AI Engineer to lead the design, deployment, and optimization of recommender and ranking models across retail loyalty programs. The role emphasizes scalable feature engineering, offline evaluation, and production-grade Python code within a hybrid work model.

You will own end-to-end experimentation, collaborate across data engineering, analytics, and product teams, and mentor engineers while improving our ML platform and

Formación

• 3+ years of experience building and deploying ML solutions in production.
• Experience with recommender systems or ranking is preferred.
• Strong Python coding and production-grade ML practices.

Responsabilidades

• Develop and deploy ML models for retail loyalty and offers personalization.
• Design offline evaluation framework with time-respecting splits and held-out AUC.
• Build scalable feature engineering pipelines in PySpark over large transaction datasets.
• Design and operate ML platform components (feature store, training pipelines, cloud jobs).
• Lead end-to-end analytical pipelines across multiple data sources and models.

Conocimientos

Python

SQL

Spark

PyTorch

TensorFlow

Airflow

BigQuery

Educación

BS/MS in CS/Math/Engineering

Herramientas

Vertex AI

SageMaker

Azure ML

Airflow

Descripción del empleo

## Senior ML/AI EngineerApply: Toronto, ON: Full time: Posted 8 Days Ago: End Date: September 22, 2026 (5 days left to apply): JR164829**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.