IT & data jobs in Canada

Explore it & data jobs in Canada. Choose a role or place to find work that fits.

Counted Oct 24, 2026

Hiring at a glance

View jobs

Open jobs: 3,864
Employers: 1,808

Advertised starting pay

Average starting payCAD / year: $96,541Across 346 listings
Average starting payCAD / hour: $48.00Across 252 listings
Average starting payUSD / year: $122,129Across 57 listings
Average starting payUSD / hour: $45.33Across 15 listings

Employer-advertised starting pay, not employee salaries. Currencies and pay periods stay separate.

Where work happens

Remote: 5%175 listings
Hybrid: 38%1,480 listings
On-site: 57%2,197 listings

Not specified: <1% (12 listings)

Employers hiring include TD (67), J&M Group (45), Capgemini (41).

Sources & calculation notes

These figures describe the listings on this role page at the date shown, not the whole job market. Listings can change.

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.

Hybrid and on-site classifications may be inferred from descriptions. Check the original ad. How we count

IT & data jobs in Canada

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StreamScan Cybersecurity

Machine Learning Specialist – Cybersecurity Products

Montreal, QC, Canada

Full-time

Apply About employer

Applications open in a new tab.

Job details

Job type: Full-time
Apply by: Nov 23, 2026
Experience: 3+ years of experience in Machine Learning or AI development.
Posting language: English
Posted: Oct 24, 2026

Original job description

Company Description

StreamScan Cybersecurity is a Canadian company specializing in advanced cyber defense for organizations in defense, aerospace, and critical infrastructure. Since 2011, the company has focused on proactive threat detection and rapid incident response, powered by proprietary AI-driven technology. StreamScan operates a 24/7 Security Operations Center (SOC) and emphasizes close collaboration, transparency, and real-time reporting for clients. Its research center develops patented, state-of-the-art cybersecurity solutions that align with international standards. The team is composed of experienced, certified cybersecurity professionals dedicated to ensuring business continuity and maximum protection.

Role Description

We are looking for a highly motivated Machine Learning Specialist to help design and develop advanced cybersecurity solutions powered by Artificial Intelligence and Machine Learning. The successful candidate will work closely with cybersecurity researchers, software engineers, SOC analysts, and product teams to build intelligent detection and response capabilities for next-generation security platforms.

Key Responsibilities

Design, develop, train, and optimize machine learning models for cybersecurity applications.
Develop behavioral analytics and anomaly detection algorithms for endpoint, network, and cloud environments.
Build AI-powered detection engines for malware, ransomware, insider threats, account compromise, and advanced persistent threats (APTs).
Develop User and Entity Behavior Analytics (UEBA) capabilities.
Create automated threat classification, prioritization, and response mechanisms.
Process and analyze large volumes of telemetry data from endpoints, networks, cloud platforms, and security logs.
Collaborate with SOC and threat intelligence teams to improve detection accuracy and reduce false positives.
Evaluate and integrate emerging AI technologies, including Large Language Models (LLMs), Generative AI, and autonomous security agents.
Participate in product architecture and roadmap discussions for XDR, EDR, NDR, and MDR solutions.

Required Technical Skills

Machine Learning & AI
Strong knowledge of supervised, unsupervised, and reinforcement learning.
Experience with anomaly detection and clustering techniques.
Deep understanding of feature engineering and model optimization.
Experience with neural networks, deep learning, and transformer architectures.
Knowledge of explainable AI (XAI) and model interpretability.
Experience with LLMs, Retrieval-Augmented Generation (RAG), and AI agents.
Programming
Advanced Python development skills.
Experience with ML frameworks:
TensorFlow
PyTorch
Scikit-learn
XGBoost
Experience developing APIs and microservices.
Data Engineering
Experience processing large-scale datasets.
Familiarity with:

*Apache Spark

*Kafka

*Vector databases

*Knowledge of MLOps practices and model lifecycle management.

Desired Qualifications

Master or PhD in Artificial Intelligence, Data Science, Cybersecurity, or related field.
3+ years of experience in Machine Learning or AI development.
Experience working on cybersecurity products or security analytics platforms.
Familiarity with threat intelligence feeds and cyber threat datasets.

Nice-to-Have Skills

Experience developing EDR/XDR detection logic.
Malware classification using AI.
Network traffic analysis and NDR technologies.
Experience with graph analytics for attack-path detection.
Knowledge of adversarial machine learning.
Experience building AI-powered SOC automation capabilities.

Apply About employer

More about this job

Responsibilities

Design, train, and optimize machine learning models and AI-powered detection capabilities for threats across endpoint, network, and cloud environments. Analyze large-scale security telemetry and collaborate with cybersecurity, SOC, threat intelligence, engineering, and product teams to improve detection, automate response, and shape security product roadmaps.

Requirements

The role seeks advanced Python development skills, strong knowledge of machine learning methods and deep learning, and experience with ML frameworks, APIs, microservices, large-scale data processing, and MLOps. Desired qualifications include a master's or PhD in a relevant field, at least three years of AI or machine learning development experience, and familiarity with cybersecurity products, threat intelligence, or security analytics.

Skills

Machine Learning
Artificial Intelligence
Cybersecurity
Python
Anomaly Detection
Behavioral Analytics
Deep Learning
Neural Networks
Transformer Architectures
Large Language Models
Retrieval-Augmented Generation
TensorFlow
PyTorch
Apache Spark
Kafka
MLOps

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Artificial Intelligence
Cybersecurity
Python
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Anomaly Detection
Clustering
Feature Engineering
Model Optimization
Neural Networks
Deep Learning
Transformer Architectures
Explainable AI
Large Language Models
Retrieval-Augmented Generation
AI Agents
TensorFlow
PyTorch
Scikit-Learn
XGBoost
APIs
Microservices
Apache Spark
Kafka
Vector Databases
MLOps
User and Entity Behavior Analytics
Extended Detection and Response

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