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Internship Applied Scientist Machine Learning Jobs in Toronto, ON

CIBC US

Applied AI Research Scientist

Toronto, ON · Hybrid

We collaborate with the leading academic institutes in the space of AI and machine learning to ... As an Applied AI Research Scientist, you will have an individual contributor (IC) role driving ...

CIBC

Applied AI Research Scientist

Toronto, ON · Hybrid

We collaborate with the leading academic institutes in the space of AI and machine learning to ... As an Applied AI Research Scientist, you will have an individual contributor (IC) role driving ...

Invision AI

Machine Learning Engineer

Toronto, ON · Hybrid

We are looking for a Machine Learning Engineer to join our Toronto team and help us take our ... an applied engineering position. We are looking for someone with a track record of building ...

Clio

Machine Learning Engineer

Toronto, ON · Hybrid

CA$129K - CA$174K/yr

Summary: We are currently seeking a Machine Learning Engineer to join our rapidly growing ... Collaborate cross-functionally with engineering, product management, operations and data science to ...

Amazon

Applied Scientist, Ring AI

Toronto, ON · On-site

Are you a passionate scientist in the computer vision area who is aspired to apply your skills to ... machine learning technology to deliver the best experience for our neighbors. This is a great ...

Invision AI

Machine Learning Engineer

Toronto, ON · On-site

We are looking for a Machine Learning Engineer to join our Toronto team and help us take our ... an applied engineering position. We are looking for someone with a track record of building ...

Td

Senior Machine Learning Engineer

Toronto, ON

CA$170K - CA$250K/yr

... Machine Learning applied to the real world. Work with large-scale, real-world datasets spanning ... Master or bachelor's degree in computer science, Statistics, Mathematics, Engineering or a related ...

Fusemachines

Lead Data Scientist

Toronto, ON · Remote

$110K - $140K/yr

Design and implement statistical models, machine learning algorithms, predictive analytics models ... Applicants should have a Masters, PhD, or advanced training in applied mathematics, engineering ...

BDO Canada

Senior Machine Learning Engineer

Toronto, ON · On-site

CA$84K - CA$128K/yr

Machine Learning Application * Convert data science prototypes into robust, scalable ML solutions. * Apply appropriate ML algorithms to structured and unstructured data problems. * Evaluate model ...

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All JobsApplied Scientist Machine Learning JobsInternship Applied Scientist Machine Learning JobsInternship Applied Scientist Machine Learning Jobs in Toronto, ON

Internship Applied Scientist Machine Learning information

What does an internship applied scientist in machine learning do?

An Internship Applied Scientist in Machine Learning works on real-world projects involving the design, development, and evaluation of machine learning models and algorithms. Their responsibilities typically include data analysis, building predictive models, experimenting with new techniques, and collaborating with engineers and researchers to solve complex problems. Interns gain hands-on experience with tools like Python, TensorFlow, or PyTorch, and contribute to advancing the company's AI capabilities. The role requires a strong foundation in mathematics, statistics, and computer science, as well as the ability to communicate findings to both technical and non-technical stakeholders.

What types of projects do internship applied scientists in machine learning typically work on, and how do they contribute to the team's goals?

Internship Applied Scientists in Machine Learning often collaborate with multidisciplinary teams to tackle real-world problems using data-driven approaches. Typical projects might include developing and fine-tuning machine learning models, conducting experiments to validate hypotheses, or assisting in the deployment of algorithms into production systems. Interns are expected to contribute fresh perspectives, help with data preprocessing, and perform thorough model evaluations. Through these projects, interns gain hands-on experience while directly supporting the team's research and product development objectives.

What are the key skills and qualifications needed to thrive as an internship applied scientist in machine learning, and why are they important?

To thrive as an Internship Applied Scientist in Machine Learning, you need a solid background in mathematics, statistics, and computer science, often supported by coursework or research experience in machine learning and data analysis. Familiarity with tools such as Python, TensorFlow, PyTorch, and experience working with large datasets are highly valued, along with knowledge of version control systems like Git. Strong problem-solving skills, curiosity, and the ability to communicate complex concepts clearly set top candidates apart. These competencies are crucial for effectively designing, implementing, and presenting machine learning solutions that address real-world challenges.

What is the difference between Internship Applied Scientist Machine Learning vs Internship Data Scientist?

AspectInternship Applied Scientist Machine LearningInternship Data Scientist
Required CredentialsRelevant degrees in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegrees in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentResearch and development teams, focus on ML model developmentBusiness teams, focus on data analysis and insights
Employer & Industry UsageTech companies, AI-focused organizationsVarious industries including tech, finance, healthcare
Comparison Search IntentUnderstanding roles in ML research and developmentUnderstanding data analysis and business insights roles

Internship Applied Scientist Machine Learning roles focus on developing and applying machine learning models, often in research settings. In contrast, Internship Data Scientist positions emphasize analyzing data to generate insights for business decisions. Both roles require strong analytical skills and relevant educational backgrounds, but they differ in their primary focus and work environment.

What are popular job titles related to Internship Applied Scientist Machine Learning jobs in Toronto, ON?

For Internship Applied Scientist Machine Learning jobs in Toronto, ON, the most frequently searched job titles are:

What job categories do people searching Internship Applied Scientist Machine Learning jobs in Toronto, ON look for?

The top searched job categories for Internship Applied Scientist Machine Learning jobs in Toronto, ON are:

Internship Applied Scientist Machine Learning jobs near you
Infographic showing various Internship Applied Scientist Machine Learning job openings in Toronto, ON as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 21% Part Time, and 1% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Machine Learning Developer (New or Recent Graduate)

Randstad Canada

Toronto, ON • On-site

Full-time

Medical, Life, Retirement

Posted 4 days ago

Job description

Application Deadline:

09/12/2026

Address:

33 Dundas Street West

Job Family Group:

Technology

BMO is seeking a Machine Learning Developer to join our team in Toronto. This role is ideal for an early-career professional with a strong foundation in Machine Learning, Python development, and AWS cloud technologies who is passionate about building intelligent, scalable solutions that solve real business challenges.

As a Machine Learning Developer, you will contribute to the design, development, testing, and deployment of machine learning applications and cloud-based solutions. You will work with cross-functional teams to translate business requirements into technical solutions, develop production-ready code, and support the end-to-end software development lifecycle.

The successful candidate will have a strong understanding of machine learning algorithms, data structures, software engineering principles, and cloud-native development. Experience developing user-facing applications, serverless functions, and machine learning models through professional experience, internships, university projects, or graduate-level research is highly valued.

Key Responsibilities

  • Design, develop, test, and implement machine learning solutions that address business needs.

  • Translate user and business requirements into technical specifications and scalable solutions.

  • Develop and maintain applications using Python and modern software engineering practices.

  • Build, deploy, and support machine learning models in cloud environments, primarily AWS.

  • Develop and integrate serverless applications using AWS services such as Lambda, API Gateway, S3, and related cloud technologies.

  • Support user interface development and integration with machine learning-powered applications.

  • Apply knowledge of machine learning algorithms, model evaluation techniques, feature engineering, and data processing.

  • Participate in model deployment, monitoring, troubleshooting, and performance optimization.

  • Ensure code and configurations adhere to security, logging, performance, and operational standards.

  • Perform root cause analysis, troubleshooting, and ongoing maintenance of applications and services.

  • Follow release management processes, version control, and CI/CD practices.

  • Evaluate emerging technologies and recommend solutions that improve performance, scalability, and user experience.

  • Collaborate with product, engineering, and business teams to deliver high-quality technology solutions.

  • Take measured risks while protecting the bank by applying BMO's Risk Management Framework and adhering to all applicable policies, standards, and regulatory requirements.


Technical Skills

Python

  • Strong programming ability using Python for application development, data processing, and machine learning implementations.

  • Experience with libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, or similar.

AWS Cloud

  • Experience developing or deploying solutions within AWS environments.

  • Understanding of serverless architectures and services such as AWS Lambda, API Gateway, S3, IAM, CloudWatch, and SageMaker.

Machine Learning

  • Strong foundation in supervised and unsupervised learning algorithms.

  • Understanding of model training, evaluation, feature engineering, optimization, and deployment best practices.

  • Knowledge of AI/ML concepts gained through professional experience, university projects, research initiatives, or graduate studies.

Qualifications

  • Approximately 1-3 years of relevant experience, including:

  • Professional work experience; and/or

  • Relevant co-op placements, internships, university projects, research initiatives, or graduate-level (MBA/Master's) projects focused on machine learning, AI, software engineering, or analytics.

  • Post-secondary degree in Computer Science, Software Engineering, Data Science, Mathematics, Engineering, AI, Machine Learning, or a related discipline.

  • Strong understanding of machine learning algorithms and statistical modeling concepts.

  • Experience developing applications using Python.

  • Exposure to AWS cloud technologies and serverless architectures.

  • Understanding of software engineering principles, testing methodologies, and the software development lifecycle.

  • Experience working with source control tools such as Git.

  • Strong analytical, problem-solving, and debugging skills.

  • Effective verbal and written communication skills.


Preferred

  • Experience with AWS SageMaker or other machine learning deployment platforms.

  • Experience building user-facing applications, dashboards, or UI components.

  • Experience with APIs, microservices, and cloud-native architectures.

  • Familiarity with CI/CD pipelines and MLOps practices.

  • Experience working in Agile delivery environments.

  • Master's degree or MBA with AI, Analytics, Data Science, or Technology-focused projects.

This role offers an excellent opportunity for a technically curious developer looking to apply machine learning, cloud technologies, and software engineering skills to impactful enterprise-scale solutions within BMO.

Salary:

$55,500.00 - $120,000.00

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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