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Senior Machine Learning Engineer

  • Toronto, ON, Canada
  • Greater Toronto Area, Canada, Canada

Pay Rate: $90-100/hour INC, $70-80/hour T4 · Contract

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Job details

Pay
Pay Rate: $90-100/hour INC, $70-80/hour T4
Job type
Contract
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Posting language
English
Posted

Original job description

Senior Machine Learning Engineer

Job Type: Contract

Contract Duration: 6 months (strong potential for extension)

Pay Rate: $90-100/hour INC, $70-80/hour T4

Location: Downtown Toronto, ON (Hybrid, 1 day onsite)

Your New Company

Join the digital division of one of Canada’s largest retail organisations, where technology is transforming how millions of Canadians discover and shop for products. The organisation builds and operates high-volume eCommerce experiences across grocery, beauty, pharmacy, loyalty, and apparel.

You will become part of a collaborative technology team applying machine learning, artificial intelligence, and emerging capabilities such as generative AI to solve complex personalisation challenges across a large-scale retail ecosystem.

Your New Role

As a Senior Machine Learning Engineer, you will take hands-on ownership of developing and improving machine learning and deep learning models that power personalised customer experiences.

Working primarily with Python and PyTorch, you will contribute across the complete ML lifecycle, from data analysis and feature preparation through experimentation, training, evaluation, and inference. This is an applied engineering role for someone who enjoys testing ideas, measuring results, and translating ML capabilities into practical business outcomes.

What You Need to Do

  • Develop, train, evaluate, and continuously improve machine learning and deep learning models.
  • Build PyTorch-based models for practical personalisation and retail use cases.
  • Gather, clean, analyse, and prepare data for model development.
  • Conduct experiments and evaluate model performance using clearly defined metrics.
  • Identify performance gaps and iterate on models based on measurable results.
  • Build reusable training, evaluation, and inference workflows.
  • Apply sound engineering practices to create maintainable and testable ML solutions.
  • Collaborate with data, product, and software engineering teams.
  • Document experiments, findings, technical decisions, and trade-offs clearly.
  • Take ownership of assigned initiatives and drive work through to completion.

What You’ll Need to Succeed

  • Strong hands-on experience developing, training, and evaluating machine learning models.
  • Advanced Python programming skills.
  • Practical experience with PyTorch or a comparable deep learning framework.
  • Strong understanding of ML fundamentals, including:
  • Training and validation
  • Model evaluation
  • Optimisation
  • Overfitting
  • Generalisation
  • Experience gathering, cleaning, analysing, and preparing data for machine learning.
  • A solid foundation in software engineering, including clean code, version control, testing, and technical documentation.
  • Demonstrated ownership and the ability to independently drive technical work forward.
  • Strong communication and cross-functional collaboration skills.

Experience with the following would be especially valuable:

  • Personalised recommendations, search, ranking, or relevance systems.
  • Supporting or operating machine learning models in production.
  • GCP and cloud-based machine learning workflows.
  • BigQuery, SQL, Spark, or Dataflow.
  • Airflow or similar workflow-orchestration technologies.
  • Kubernetes and containerised ML workloads.

What You’ll Get in Return

You will have the opportunity to work with highly skilled professionals on advanced machine learning problems that directly influence digital experiences used by millions of Canadians.

The organisation offers competitive compensation, continuous learning and professional development opportunities, and exposure to modern machine learning and AI technologies. You will work in an inclusive and supportive environment that values authenticity, trust, collaboration, and diversity of thought.

This is an opportunity to take meaningful ownership, deliver ML solutions at significant scale, and help shape the future of digital commerce in Canada.

What You Need to Do Now

If you’re interested in this role, click “Apply Now” to forward an up-to-date copy of your CV, or contact us today.

If this position isn’t quite right for you but you are considering a new opportunity, please contact us for a confidential discussion about your career.

This posting is for an existing vacancy with the organisation.

AI may be used to screen, assess, or select applicants for the position.

More about this job

Responsibilities

Develop, train, evaluate, and improve machine learning and deep learning models for personalized retail experiences, using Python and PyTorch across the ML lifecycle. Build reusable training, evaluation, and inference workflows, collaborate across teams, document technical decisions, and drive assigned initiatives to completion.

Requirements

Requires strong hands-on machine learning experience, advanced Python skills, practical experience with PyTorch or a comparable framework, and a solid understanding of model training, evaluation, optimization, overfitting, and generalization. Candidates should also have data preparation and software engineering experience, demonstrate ownership and collaboration, and ideally bring experience with recommendation or ranking systems, production ML, cloud workflows, and related data and orchestration tools.

Skills

  • Machine Learning
  • Deep Learning
  • Python
  • PyTorch
  • Model Training
  • Model Evaluation
  • Data Preparation
  • Feature Engineering
  • Personalized Recommendations
  • Search and Ranking
  • Production Machine Learning
  • GCP
  • BigQuery
  • SQL
  • Spark
  • Airflow

Benefits

  • Competitive Compensation
  • Continuous Learning
  • Professional Development

Visa sponsorship

Not detected in the job text

Categories

  • Technology
  • Software
  • Data & Analytics
  • Retail
  • Engineering

Keywords

  • Machine Learning
  • Deep Learning
  • Artificial Intelligence
  • Generative AI
  • Python
  • PyTorch
  • Model Training
  • Model Evaluation
  • Model Optimization
  • Data Analysis
  • Data Cleaning
  • Data Preparation
  • Feature Preparation
  • Inference
  • Personalization
  • Recommendations
  • Search
  • Ranking
  • Relevance Systems
  • Production Machine Learning
  • GCP
  • BigQuery
  • SQL
  • Spark
  • Dataflow
  • Airflow
  • Kubernetes
  • Containerized Workloads
  • Version Control
  • Software Testing

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