Jobs in Ontario

Jobs across Ontario, Canada. Choose a city or town to search nearby.

203,027 listings · 20,948 employers Counted Nov 5, 2026. How we count

About these jobs

Employers hiring include Loblaw Companies Limited (5,176 listings), Shoppers Drug Mart (4,002 listings), Walmart Canada (3,337 listings).

Common roles: Retail sales associate, Practical nurse, Registered nurse.

33,104 of these listings include employer-provided pay. Open a job to see its currency and pay period.

Advertised starting pay
Pay period	Median starting pay	Listings
CAD / hour	23.00	17,722
CAD / year	80,000.00	14,038
USD / year	155,000.00	83
CAD / month	4,500.00	63
CAD / week	1,500.00	40
CAD / day	101.00	23
USD / hour	19.50	12

Employer-listed minimums from at least 10 listings per currency and pay period. These are advertised starting amounts, not employee salaries. Jobs without a minimum and unusual amounts are excluded.

Workplace classifications: 0% remote, 17% hybrid, 82% on-site, 0% not specified. Hybrid and on-site classifications may be inferred from descriptions. Check the original ad.

Based on the dated count above. Listings can change. How we count

Jobs in Ontario

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Added 6 hours ago
L
Seasonal Full Time Educator |Vaughan Mills
lululemon
Vaughan, ON
- Full-time
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DG
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Toronto, ON
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Toronto, ON
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Gravenhurst, ON
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Ottawa, ON
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Toronto, ON
- $144,000 to $194,400 CAD / year
- Full-time
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Kawartha Lakes, ON
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Ross Memorial Hospital
Kawartha Lakes, ON
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Registered Nurse - Day Hospital
Ross Memorial Hospital
Kawartha Lakes, ON
- Part-time
Added 6 hours ago
RM
RN Registered Nurse
Ross Memorial Hospital
Kawartha Lakes, ON
- Full-time

vTech Solution Canada

Senior Machine Learning Engineer

Toronto, ON, Canada

Contract

Apply About employer

Applications open in a new tab.

Job details

Job type: Contract
Apply by: Dec 4, 2026
Posting language: English
Posted: Nov 4, 2026

Original job description

Job Title: Machine Learning Engineer - Senior

Location: Toronto, ON

Work Mode: Hybrid (3 days a week onsite)

Description

The Senior Machine Learning Engineer will deploy, integrate, monitor, and operationalize AI and machine-learning solutions using modern MLOps practices. The role will establish reusable deployment patterns, tooling, monitoring, and automation that enable AI solutions to move reliably from development into production and scale across the enterprise.

Responsibilities:

Creates machine learning models and utilizes data to train models
Focuses on analyzing data to find relations between the input and the desired output
Understands business objectives and develops models that help achieve them, along with metrics to track their progress
Designs and develops machine learning and deep learning systems
Runs machine learning tests and experiments Implements appropriate machine learning algorithms

General Skills:

Experience managing available resources such as hardware, data, and personnel so that deadlines are met Experience analyzing the machine learning algorithms that could be used to solve a given problem and ranking them by their success probability
Experience exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world Experience verifying data quality, and/or ensuring it via data cleaning
Experience supervising the data acquisition process if more data is needed
Experience finding available datasets online that could be used for training
Experience defining validation strategies Experience defining the preprocessing or feature engineering to be done on a given dataset
Background in statistics and computer programming
A team player with a track record for meeting deadlines, managing competing priorities and client relationship management experience

Must Haves:

Advanced hands-on experience with Azure Machine Learning, Microsoft Foundry/Azure AI Foundry, MLOps, CI/CD, containers, APIs, automation, model deployment, monitoring and observability.
Strong Python and machine-learning engineering skills with demonstrated experience taking models from experimentation into reliable production environments. Tech stack: Python, SQL, Power BI, Power Automate, VSCode, Git, Jira, Azure (ML Studio, AI Foundry), Databricks.
Deep understanding of ML algorithms and statistics, including classification, regression, clustering, feature engineering, model evaluation, hyperparameter tuning, drift, retraining, performance optimization and production model troubleshooting.

Public Sector Experience: preferred but not required

Apply About employer

More about this job

Responsibilities

Develop, deploy, integrate, monitor, and operationalize machine-learning solutions, establishing reusable MLOps patterns and automation to move models reliably into production. Analyze data, design and test models, and track their performance to support business objectives and enterprise-scale deployment.

Requirements

Requires advanced hands-on experience with Azure Machine Learning or Azure AI Foundry, MLOps, CI/CD, containers, APIs, automation, deployment, monitoring, and observability, along with strong Python and machine-learning engineering skills. Candidates should have deep knowledge of machine-learning algorithms and statistics, production model troubleshooting, and experience with data preparation, validation, evaluation, tuning, drift, and retraining; public-sector experience is preferred but not required.

Skills

Python
Machine Learning
MLOps
Azure Machine Learning
Azure AI Foundry
CI/CD
Model Deployment
Model Monitoring
Containers
APIs
Automation
SQL
Databricks
Statistics
Feature Engineering
Model Evaluation

Visa sponsorship

Not detected in the job text

Keywords

Python
SQL
Power BI
Power Automate
Visual Studio Code
Git
Jira
Azure Machine Learning
Azure AI Foundry
Databricks
MLOps
CI/CD
Containers
APIs
Automation
Model Deployment
Model Monitoring
Observability
Machine Learning
Deep Learning
Classification
Regression
Clustering
Feature Engineering
Model Evaluation
Hyperparameter Tuning
Data Cleaning
Data Visualization
Model Drift
Model Retraining

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