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Machine Learning Engineer Jobs in Ontario (NOW HIRING)

Senior Machine Learning Engineer

Toronto, ON · On-site

CA$84K - CA$128K/yr

Advanced programming skills in Python, with practical experience using popular machine learning libraries such as scikit-learn, TensorFlow, and/or PyTorch. Capable of building, tuning, and deploying ...

Senior Machine Learning Engineer

London, ON · On-site

CA$84K - CA$128K/yr

Advanced programming skills in Python, with practical experience using popular machine learning libraries such as scikit-learn, TensorFlow, and/or PyTorch. Capable of building, tuning, and deploying ...

Senior Machine Learning Engineer

Ottawa, ON · On-site

CA$84K - CA$128K/yr

Advanced programming skills in Python, with practical experience using popular machine learning libraries such as scikit-learn, TensorFlow, and/or PyTorch. Capable of building, tuning, and deploying ...

As a Machine Learning Engineer, you will: * Join a world-class team of AI developers with an extensive track record of shipping solutions at the cutting-edge * Build scalable machine learning ...

We are looking for a Sr. Machine Learning Engineer to help translate raw data into meaningful insights that drive strategic decision-making. The Opportunity Summary We are seeking an experienced ...

As a Machine Learning Engineer, you will: * Join a world-class team of AI developers with an extensive track record of shipping solutions at the cutting-edge * Architect scalable machine learning and ...

What you'll do As a machine learning engineer, you will be responsible for analyzing opportunities, proposing ideas, training & evaluating ML models, running experiments, and deploying everything to ...

Day-to-day as a Machine Learning Engineer: * Join a world-class team of AI developers with an extensive track record. * Architect scalable machine learning and Gen AI systems that integrate with ...

Machine Learning Engineer, Edge AI

Waterloo, ON · On-site

CA$120K - CA$170K/yr

We are looking for a Machine Learning Engineer, Edge AI to lead the integration and control of our next-generation AI accelerators. As our products evolve to include dedicated neural network hardware ...

Join EvenUp as a Staff Machine Learning Engineer and help set the technical direction for how machine learning powers Piai, our proprietary claims‑intelligence platform. This is a technical ...

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ...

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Machine Learning Engineer information

See Ontario salary details

$64.5K

$143K

$218.5K

How much do machine learning engineer jobs pay per year?

As of Jul 24, 2026, the average yearly pay for machine learning engineer in Ontario is $142,956.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $166,000.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

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 strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Ontario? The most popular types of Machine Learning Engineer jobs in Ontario are:
What are popular job titles related to Machine Learning Engineer jobs in Ontario? For Machine Learning Engineer jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in Ontario look for? The top searched job categories for Machine Learning Engineer jobs in Ontario are:
What are popular job titles related to Machine Learning Engineer jobs in ON? For Machine Learning Engineer jobs in ON, the most frequently searched job titles are:
Infographic showing various Machine Learning Engineer job openings in Ontario as of July 2026, with employment types broken down into 93% Full Time, 4% Part Time, and 3% Contract. Highlights an 86% Physical, 6% Hybrid, and 8% Remote job distribution, with an average salary of $142,956 per year, or $68.7 per hour.

Senior Machine Learning Engineer

BDO Canada

Toronto, ON • On-site

CA$84K - CA$128K/yr

Full-time

PTO

Posted yesterday


Job description

Putting people first, every day

BDO is a firm built on a foundation of positive relationships with our people and our clients. Each day, our professionals provide exceptional service, helping clients with advice and insight they can trust. In turn, we offer an award-winning environment that fosters apeople-first culturewith a high priority on your personal and professional growth.

Your Opportunity

BDO Digital is seeking an experienced and technically proficient Senior ML Engineer to join our Technology Advisory Services practice. This role blends technical leadership with hands-on MLOps development, focusing on delivering scalable, secure, and production-grade machine learning pipelines using Azure and Databricks. The successful candidate will also lead client engagements, aligning ML initiatives with business objectives to create real-world impact.

Key Responsibilities

MLOps and Platform Development

  • Design and implement end-to-end MLOps pipelines using Databricks, MLflow, and related tools.
  • Build and manage scalable data and feature engineering pipelines in Databricks.
  • Automate model lifecycle processes including training, testing, deployment, and monitoring.
  • Implement CI/CD workflows using tools such as Azure DevOps or GitHub Actions.
  • Ensure operational reliability, performance, and compliance across all ML workflows.

Client and Delivery Management

  • Serve as a primary technical lead for client engagements focused on ML solution delivery.
  • Translate business goals into machine learning strategies and operational plans.
  • Collaborate with cross-functional teams to integrate models into production environments.

Machine Learning Application

  • Convert data science prototypes into robust, scalable ML solutions.
  • Apply appropriate ML algorithms to structured and unstructured data problems.
  • Evaluate model performance, run experiments, and iterate for improvement.
  • Document ML pipelines and contribute to internal knowledge sharing.

Qualifications

Required

  • Educational Background
    A Bachelor's or master's degree in computer science, Data Science, Engineering, or a closely related discipline. A strong academic foundation in algorithms, data structures, machine learning, and distributed systems is essential.
  • Professional Experience
    A minimum of 5 years of hands-on experience in software engineering, data engineering, or DevOps, including at least 3 years of direct experience in MLOps or machine learning engineering roles. Proven success in deploying and maintaining machine learning solutions in production environments is expected.
  • In-depth knowledge of the Microsoft Azure ecosystem, with demonstrated experience using services such as Azure Machine Learning, Azure Data Lake, Azure Kubernetes Service (AKS), and Azure DevOps. Ability to leverage cloud-native tools to build scalable and secure ML workflows.
  • Very Strong proficiency with Databricks, including hands-on work with Delta Lake, MLflow, and Apache Spark. Experience integrating these tools into MLOps pipelines and optimizing performance and reliability in production.
  • Advanced programming skills in Python, with practical experience using popular machine learning libraries such as scikit-learn, TensorFlow, and/or PyTorch. Capable of building, tuning, and deploying ML models in real-world applications.
  • Solid understanding of CI/CD practices, with experience designing and maintaining pipelines using tools like GitHub Actions, Azure DevOps, or Jenkins. Familiarity with infrastructure-as-code tools such as Terraform or ARM templates for automating environment provisioning and deployment.
  • Excellent written and verbal communication skills, with the ability to effectively engage with a range of stakeholders, including data scientists, engineers, business partners, and executive leadership. Proven ability to explain technical concepts to non-technical audiences and influence decision-making.

Preferred

  • Certifications
    Professional certifications such as Databricks Certified Professional or Microsoft Certified, e.g. Azure Solution Architect.
  • Strong knowledge of LLM frameworks and libraries (such as transformers, trl, deepspeed, PyTorch), and exposure to various ML techniques and their practical implementation in production at large scale.
  • Consulting experience and someone with excellent communication and client management skills

The expected range of compensation for this role is $84,000 to $128,000. This job posting is a pipelining exercise for upcoming engagements.


Why BDO?
Our people-first approach to talent has earned us a spot among Canada's Top 100 Employers for 2026. This recognition is a milestone we're thrilled to add to our collection of awards for both experienced and student talent experiences.

Our firm is committed to providing an environment where you can be successful in the following ways:

  • We enable you to engage with how we change and evolve, being a key contributor to the success and growth of BDO in Canada.

  • We help you become a better professional within our services, industries, and markets with extensive opportunities for learning and development.

  • We support your achievement of personal goals outside of the office and making an impact on your community.

Giving back adds up: Where company meets community. BDO is actively involved in our communities by supporting local charity initiatives. We support staff with local and national events where you will be given the opportunity to contribute to your community.


Total rewards that matter: We pay for performance with competitive total cash compensation that recognizes and rewards your contribution. We provide flexible benefits from day one, and a market leading personal time off policy. We are committed to supporting your overall wellness beyond working hours and provide reimbursement for wellness initiatives that fit your lifestyle.


Everyone counts: We believe every employee should have the opportunity to participate and succeed. Through leadership by our Diversity, Equity and Inclusion Leader, we are committed to a workplace culture of respect, inclusion, and diversity. We recognize and celebrate the valuable differences among each of us, including race, religious beliefs, physical or mental disabilities, age, place of origin, marital status, family status, gender or gender identity and sexual orientation. If you require accommodation to complete the application process, please contact us.

Flexibility: All BDO personnel are expected to spend some of their time working in the office, at the client site, and virtually unless accommodations or alternative work arrangements are in place.

Our model is a blended approach designed to support the flexible needs of our people, the firm and our clients. It's about creating work experiences that meet everyone's needs and providing flexibility to adjust when, where and how we work to meet the expectations of our role.

Code of Conduct: Our Code of Conduct sets clear standards for how we conduct business. It reflects our shared values and commitments and includes guiding principles to help us make ethical decisions and maintain trust with each other, our clients, and the public.

BDO may use artificial intelligence enabled tools to support certain aspects of the recruitment process. While these tools assist our teams, our use of AI does not replace human decision making, and all employment-related outcomes are made by BDO personnel.

More information on BDO Canada's Privacy Policy can be found here: Privacy Policy | BDO Canada

Ready to make your mark at BDO? Click "Apply now" to send your up-to-date resume to one of our Talent Acquisition Specialists.

To explore other opportunities at BDO, check out ourcareers page.

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