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Junior Machine Learning Engineer Jobs in Ontario

Td

Machine Learning Engineer II

Toronto, ON · On-site

CA$154K - CA$199K/yr

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 ...

Proton

Senior Machine Learning Engineer (SOC)

Paris, ON · On-site

The Team The Security Machine Learning Engineer will play a key role in transforming our Security Operations Center (SOC) from reactive to proactive by integrating advanced machine learning and data ...

Career Renew

Senior Machine Learning Engineer

Toronto, ON · Remote

$165K - $225K/yr

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 ...

Career Renew

Senior Machine Learning Engineer

Toronto, ON · Remote

$165K - $225K/yr

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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All JobsMachine Learning Engineer JobsJunior Machine Learning Engineer JobsJunior Machine Learning Engineer Jobs in Ontario

Junior Machine Learning Engineer information

See Ontario salary details

$26K

$119.2K

$207.5K

How much do junior machine learning engineer jobs pay per year?

As of Sep 4, 2026, the average yearly pay for junior machine learning engineer in Ontario is $119,158.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,500.00 and $149,000.00 per year, depending on experience, location, and employer.

What does a junior machine learning engineer do?

As a junior machine learning engineer, you work in AI, performing research with algorithms and data modeling techniques. Machine learning involves using large collections of data to create systems that are capable of making predictions, and in this field, your duties and responsibilities revolve around using advanced mathematics to design applications for use in everything from stock trading to sports betting. Some machine learning efforts involve images, and this branch of the field is known as computer vision, while other techniques which focus on text are called natural language processing (NLP). Given these divisions, titles in machine learning include computer vision engineer, NLP scientist, or simply research scientist.

What kinds of projects and responsibilities can a junior machine learning engineer expect in their first year on the job?

As a Junior Machine Learning Engineer, you’ll typically work on tasks such as data preprocessing, building and testing simple models, and supporting more senior engineers in deploying machine learning solutions. Your responsibilities may also include cleaning datasets, implementing basic algorithms, and running experiments to evaluate model performance. You’ll often collaborate closely with data scientists, software engineers, and product teams to understand project goals and learn best practices. The role provides excellent opportunities to develop your technical skills, gain exposure to various stages of the ML pipeline, and gradually take on more complex projects as you grow.

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

To succeed as a Junior Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of algorithms and statistics, and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch and tools like scikit-learn, as well as experience with version control systems like Git, are typically required. Strong problem-solving abilities, attention to detail, and a willingness to learn from feedback are valuable soft skills that help you adapt and grow in the field. These skills ensure you can effectively develop, test, and improve machine learning models while collaborating with more experienced engineers and contributing to team projects.

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

AspectJunior Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often advanced certifications
Work EnvironmentDeveloping and deploying ML models, coding, testingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, tech, consulting
Search & Comparison IntentYesYes

While both roles involve working with data and machine learning, Junior Machine Learning Engineers focus on building and deploying models, often with coding and engineering skills. Data Scientists analyze data, create statistical models, and interpret insights. The roles overlap but differ mainly in their core responsibilities and skill emphasis.

How much do junior machine learning engineers make?

Junior machine learning engineers typically earn between $70,000 and $100,000 annually, depending on location, education, and industry. Entry-level roles often require knowledge of programming languages like Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.
More about Junior Machine Learning Engineer jobs

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The most popular types of Machine Learning Engineer jobs in Ontario are:

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For Junior Machine Learning Engineer jobs in Ontario, the most frequently searched job titles are:

What job categories do people searching Junior Machine Learning Engineer jobs in Ontario look for?

The top searched job categories for Junior Machine Learning Engineer jobs in Ontario are:

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Cities in Ontario with the most Junior Machine Learning Engineer job openings:

Junior Machine Learning Engineer jobs near you
Infographic showing various Junior Machine Learning Engineer job openings in Ontario as of August 2026, with employment types broken down into 1% As Needed, 69% Full Time, 28% Part Time, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $119,158 per year, or $57.3 per hour.

Senior Machine Learning Developer

BMO Capital Markets

Toronto, ON

Full-time

Medical, Life, Retirement

Posted 2 days ago

New

Job description

Application Deadline:

10/01/2026

Address:

33 Dundas Street West

Job Family Group:

Technology

Location: Toronto, ON (Hybrid)

Overview

BMO is seeking a Senior Machine Learning Engineer to join our team in Toronto. This role is ideal for an experienced technology professional with expertise in Machine Learning, Python development, AWS cloud technologies, and solution design who is passionate about leading the delivery of innovative AI and machine learning solutions at enterprise scale.

As a Senior Machine Learning Engineer, you will lead complex machine learning and AI initiatives from concept through implementation. Working closely with business stakeholders, product teams, architects, data engineers, and technology partners, you will translate business objectives into scalable technical solutions and drive projects through all stages of the delivery lifecycle.

The successful candidate will combine strong technical expertise with solution design and project leadership capabilities. You will be responsible for leading the delivery of strategic initiatives, influencing technical direction, and ensuring the successful implementation of secure, scalable, and high-performing machine learning solutions that deliver measurable business value.

Key Responsibilities

  • Lead the delivery of machine learning and AI initiatives from requirements definition through design, development, deployment, and production support.

  • Partner with business and technology stakeholders to understand objectives, define technical approaches, and develop implementation roadmaps.

  • Drive end-to-end execution of complex projects, coordinating activities across engineering, data, infrastructure, security, and platform teams.

  • Design scalable, resilient, and maintainable machine learning solutions aligned with enterprise architecture standards and business objectives.

  • Develop and maintain production-ready applications and machine learning services using Python and modern software engineering practices.

  • Lead solution design activities and contribute to architectural decisions that support long-term scalability, reliability, and operational excellence.

  • Design and implement cloud-native applications and services leveraging AWS technologies.

  • Build, deploy, and optimize machine learning models in production environments.

  • Drive the adoption of MLOps practices, including CI/CD, model lifecycle management, monitoring, automation, and observability.

  • Ensure solutions meet security, compliance, performance, and operational requirements.

  • Identify project risks, dependencies, and technical challenges, developing mitigation strategies to support successful delivery.

  • Collaborate with Enterprise Architecture and engineering teams to ensure alignment with broader technology strategies and standards.

  • Evaluate emerging technologies and recommend improvements that enhance platform capabilities, scalability, and business outcomes.

  • Lead troubleshooting and root cause analysis activities for complex production issues.

  • Support project planning, estimation, and technical delivery activities across multiple concurrent initiatives.

  • Apply BMO's Risk Management Framework and adhere to all applicable regulatory, security, and governance standards.

Required Technical Skills

Python & Software Engineering

  • Advanced proficiency in Python application development.

  • Strong experience building enterprise-grade applications, APIs, and machine learning services.

  • Hands-on experience with libraries and frameworks such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, FastAPI, or equivalent technologies.

  • Strong understanding of software design patterns, testing methodologies, code quality standards, and modern development practices.

AWS Cloud & Solution Design

  • Strong experience designing and implementing cloud-native solutions in AWS.

  • Experience with AWS services including SageMaker, Lambda, API Gateway, S3, IAM, CloudWatch, EventBridge, ECS, EKS, and related technologies.

  • Strong knowledge of serverless, distributed, and event-driven architectures.

  • Proven ability to design scalable, secure, and highly available solutions supporting enterprise workloads.

Machine Learning & MLOps

  • Experience developing, deploying, monitoring, and optimizing machine learning models in production environments.

  • Strong understanding of machine learning techniques, feature engineering, model evaluation, and deployment best practices.

  • Experience implementing MLOps capabilities, CI/CD pipelines, automation frameworks, and model governance practices.

  • Knowledge of model monitoring, observability, and operational excellence principles.

Qualifications

Required

  • 7+ years of experience in Software Engineering, Machine Learning Engineering, Artificial Intelligence, or a related technology discipline.

  • Proven experience leading the delivery of complex machine learning, AI, data, or cloud technology initiatives within enterprise environments.

  • Demonstrated experience translating business requirements into technical solutions, architecture designs, and implementation plans.

  • Strong experience working across cross-functional teams, including business, data, engineering, and infrastructure stakeholders.

  • Advanced proficiency in Python development.

  • Strong experience designing and implementing cloud-native solutions using AWS.

  • Experience with APIs, microservices, distributed systems, and modern architecture patterns.

  • Strong understanding of software engineering principles, DevOps practices, testing methodologies, and the software development lifecycle.

  • Experience with Git, CI/CD pipelines, and Agile delivery methodologies.

  • Excellent analytical, problem-solving, communication, and stakeholder management skills.

Preferred

  • Experience delivering enterprise-scale AI and Machine Learning platforms and solutions.

  • Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI-powered applications.

  • Experience with AWS SageMaker and cloud-based machine learning platforms.

  • Experience with containerization technologies, Kubernetes, and platform engineering practices.

  • AWS certifications such as Solutions Architect or Machine Learning Specialty.

  • Experience within financial services or other highly regulated industries.

  • Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Mathematics, or a related field.

Why Join BMO?

This is an opportunity to lead strategically important AI and Machine Learning initiatives that drive innovation across the bank. You will work on enterprise-scale solutions, influence technology direction, and partner with diverse teams to deliver impactful outcomes while helping shape the future of AI and advanced analytics at BMO.

Salary:

$70,000.00 - $150,000.00

Pay Type:

Salaried

The above represents BMO Financial Group's pay range and type.

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group's expected target for the first year in this position.

BMO Financial Group's total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit:https://jobs.bmo.com/global/en/Total-Rewards

About Us

At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world.

As a member of the BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one - for yourself and our customers. We'll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we'll help you gain valuable experience, and broaden your skillset.

To find out more visit us at https://jobs.bmo.com/ca/en.

BMO is committed to an inclusive, equitable and accessible workplace. By learning from each other's differences, we gain strength through our people and our perspectives. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation, please contact your recruiter.

Note to Recruiters: BMO does not accept unsolicited resumes from any source other than directly from a candidate. Any unsolicited resumes sent to BMO, directly or indirectly, will be considered BMO property. BMO will not pay a fee for any placement resulting from the receipt of an unsolicited resume. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.

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