1

Internship Applied Scientist Machine Learning Jobs in Toronto, ON

Machine Learning Engineer

Toronto, ON ยท Remote

CA$110K - CA$130K/yr

Bachelor's or Master's degree in Computer Science, Machine Learning, or equivalent field * 3+ years of industry experience in machine learning roles * Deep expertise in deep learning and computer ...

Research Machine Learning Scientist

Toronto, ON ยท On-site

CA$140K - CA$250K/yr

As a Research Machine Learning Scientist, you will * Join a world-class team of machine learning researchers with an extensive track record in both academia and industry. * Research, develop, and ...

Machine Learning Engineer

Toronto, ON ยท On-site

CA$120K - 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 ...

... Applied Science talent to help us build new, science-backed services that drive success for our ... Deep Learning, Reinforcement Learning and/or Recommender Systems. You have the scientific and ...

As a Machine Learning Engineer Lead, you will join our Data Science & AI Pod, focused on designing ... Data Science, Applied AI, Software Engineering, or related disciplines * Bachelor's or Master ...

Graduate degree in Computer Science with a strong background in machine learning required. * Strong problem-solving abilities, solid background in algorithms and data structures required. * Strong ...

Senior / Staff Applied Scientist

Toronto, ON ยท On-site +1

CA$146K - CA$280K/yr

Qualifications: - Minimum of 6+ years of professional experience in an applied data science, advanced analytics, or machine learning role, with a track record of driving end-to-end projects. - MS/PhD ...

Machine Learning Engineer II

Toronto, ON ยท On-site

CA$154K - CA$199K/yr

... problems in Machine Learning applied to the real world. Our team is building standardized ... Minimum three years of experience delivering major data science projects in large, complex ...

Showing results 21-40

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:

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 Engineer

Clarius Mobile Health

Toronto, ON โ€ข Remote

CA$110K - CA$130K/yr

Temporary

Posted 10 days ago


Job description

A Career at Clarius

Today, as many as 25 million medical professionals globally don’t have access to medical imaging, which is proven to improve patient care and reduce healthcare costs. That’s why we’re on a mission to make medical imaging accessible everywhere by delivering high-performance, affordable, and easy-to-use solutions powered by artificial intelligence and connected to the cloud.

By making Clarius your next career move, you’re joining a team of 150+ people who are talented, innovative, and highly collaborative. You’re also joining a community that includes thousands of physicians worldwide who use Clarius to deliver better patient care! And you’re joining a thrice-certified Great Place to Work! 


Your Role

Clarius Mobile Health is seeking a Machine Learning Engineer to contribute to a special project focused on expanding access to ultrasound technology while advancing our next-generation innovations.

Over the next 24 months, you'll focus on the development, training, and deployment of machine learning models in both research and production settings. This role requires strong software engineering practices and the ability to find creative solutions that continually improve the performance and safety of ML in production.

If we meet our project milestones, there's a strong possibility of this role being extended or turning into a permanent position


Your Responsibilities Include:

  • Develop, train, and deploy machine learning models for both research and production environments
  • Improve and maintain ML tools, ensuring robustness and scalability
  • Design, optimize, and maintain ML data pipelines for efficient processing and training
  • Automate ML tasks, model retraining, evaluation, and monitoring in production
  • Collaborate with researchers and software engineers to transfer ML research into practical applications
  • Address and minimize ML technical debt, ensuring maintainable and efficient code
  • Optimize model performance and inference speed for production deployment

Your Experience so Far: 

  • Bachelor's or Master's degree in Computer Science, Machine Learning, or equivalent field
  • 3+ years of industry experience in machine learning roles
  • Deep expertise in deep learning and computer vision
  • Mastery in Python development with Unix/Linux
  • Proficiency in TensorFlow and PyTorch
  • Software engineering best practices (version control, testing, code quality)
  • Ability to communicate ML concepts to technical audiences

Preferred:

  • C++ and Javascript development
  • Building scalable web applications and ML tools
  • Model optimization and inference speed tuning
  • Cloud platforms and deployment (Docker, Kubernetes, AWS, GCP)


Location: Vancouver, BC (hybrid preferred; remote candidates will be considered)

Contract Duration: Two-year fixed-term through October 2028, with strong likelihood of extension upon meeting project deliverables

Salary Range: $110,000 - $130,000 CAD


More Reasons to Consider:

Be Part of a High-Impact Project – You’ll play a key role in creating a more affordable ultrasound solution, making medical imaging more accessible worldwide.


Work for a Three-Time Certified Great Place to Work – Clarius has been recognized three times as a great place to work, and you’ll be joining a company that values innovation, teamwork, and employee well-being.


Workspace: Our modern office features sit/stand desks, various health & wellness facilities, a stocked kitchen, outdoor amenities, on-site daycare, enclosed parking, a free on-site gym and close proximity to a SkyTrain station.



Clarius Mobile Health is proud to be an Equal Opportunity Employer. We encourage applications from any qualified candidate regardless of ethnicity, religion, age, national origin, disability status, sexual orientation, gender identity or expression. Please let us know if you require any accommodations during the interview process.


Use of Artificial Intelligence in the Recruitment Process

As part of our recruitment and assessment process, we may use artificial intelligence tools to support certain activities. This may include, but is not limited to, reviewing applications, supporting candidate assessments, and conducting or facilitating one way video interviews.

Where AI tools are used, they are intended to support our recruitment process and do not replace human decision making. Candidates may be informed where AI is used as part of an assessment and will have the opportunity to engage with the recruitment process in accordance with applicable laws and our recruitment practices.



Requirements:
  • Bachelor's or Master's degree in Computer Science, Machine Learning, or equivalent field
  • 3+ years of industry experience in machine learning roles
  • Strong background in machine learning and artificial intelligence with expertise in deep learning and computer vision
  • Mastery in Python development with Unix/Linux. You strive for clean, correct code while iterating on experiments in Python
  • Knowledge of common ML frameworks: TensorFlow and PyTorch
  • Experience in software engineering best practices, including version control, testing, and code quality
  • Ability to explain and present analyses and machine learning concepts to a broad technical audience
  • A passion for making ML methods robust, scalable, and maintainable

Preferred:

  • C++ and Javascript development experience
  • Experience with building scalable web applications and ML tools
  • Experience optimizing model performance and inference speed
  • Familiarity with cloud platforms and deployment tools (Docker, Kubernetes, AWS, GCP)

What You'll Bring:

  • Strong problem-solving skills and ability to tackle complex ML engineering challenges
  • Collaborative mindset, thriving in cross-functional teams
  • Commitment to continuous improvement and learning
  • Initiative to address technical challenges and drive innovation