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Machine Learning Engineer Opt Jobs in Toronto, ON

ServiceNow

Senior Machine Learning Engineer

Toronto, ON · On-site +1

The Machine Learning Developer designs, builds, ships, and operates applications whose core behavior is model-driven rather than explicitly authored. The Developer builds the engine behind ServiceNow ...

Thri5 Inc.

AI / Machine Learning Engineer

Toronto, ON · On-site

Your Role As an AI / Machine Learning Engineer at Thri5, you'll help build the agent layer that powers our System of Actions. You'll design and implement multi-agent Co-pilot systems that orchestrate ...

YO AI Labs

Machine Learning Engineer - Remote

Toronto, ON · Remote

$80 - $120/hr

Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software ...

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Machine Learning Engineer Opt information

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

What are some common challenges machine learning engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What is the difference between Machine Learning Engineer Opt vs Data Scientist?

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

What are popular job titles related to Machine Learning Engineer Opt jobs in Toronto, ON?

For Machine Learning Engineer Opt jobs in Toronto, ON, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Opt jobs in Toronto, ON look for?

The top searched job categories for Machine Learning Engineer Opt jobs in Toronto, ON are:

Machine Learning Engineer Opt jobs near you
Infographic showing various Machine Learning Engineer Opt job openings in Toronto, ON as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer (Detection, TOR)

Doppel

Toronto, ON • On-site

$80 - $110/hr

Other

PTO

Re-posted 21 days ago

Job description

Why Join Doppel

Doppel is built to outsmart one of the great threats AI presents: mass-manufactured social engineering. Countless scams, deepfakes, and other social engineering attacks are surging across every digital channel: websites, social media, ads, encrypted messaging apps, mobile, and more. Our mission is simple but bold: make the internet a safer place by outsmarting the world’s fastest-evolving digital threats.

Backed by a16z and Bessemer and trusted by some of the world’s most recognized brands (OpenAI, United Airlines, Coinbase, etc.), Doppel is growing fast. If you’re driven to solve real-world problems with bold technology, we’d love to meet you.

What We're Building

We're building the AI-native social engineering defense platform.

This means we're designing scalable systems that monitor billions of domains, social media accounts, apps, dark web forums, etc., and leverage AI agents to identify and neutralize digital threats.

What We're Looking For

We’re looking for a machine learning engineer to help build and scale the models and systems that power Doppel’s detection systems. As an MLE at Doppel, you will

  • Design, train, and deploy models for both batch and real-time inference that identify malicious or infringing content across diverse data sources.
  • Partner closely with the Detection and Infrastructure teams to ensure our ML systems scale with the volume of web data we ingest.
  • Work on problems that range from NLP and embeddings to similarity search, classification, and anomaly detection.
  • Collaborate directly with customers and internal stakeholders to translate real-world threats into production ML systems.

You may be a fit if you:

  • Have experience building and deploying ML systems in production environments.
  • Are comfortable working with large-scale datasets and distributed data processing frameworks.
  • Understand the trade-offs between research-quality models and production-ready systems.
  • Are excited about solving real-world problems where the adversary is constantly evolving.
What We Offer

A mission-driven culture with low ego, high ownership, deep customer obsession, and exceptional talent density.

Free lunch and dinner in the office.

Flexible PTO.

Quarterly team offsites.

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