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Trainee Machine Learning Engineer Jobs in Ontario

Quincus

Machine Learning Engineer

Toronto, ON · On-site

As a machine learning engineer, you will be responsible for designing and implementing scalable systems for serving models, optimizing inference performance, and managing production workflows.

NTENT

Machine Learning Engineer

Toronto, ON · Remote

Machine Learning Engineer Position: Full time Location: Toronto, Ontario (Initially Remote) About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize ...

Magna

Machine Learning Engineer

Milton, ON · On-site

The Machine Learning Engineer will play a pivotal role in driving innovation and operational efficiency through data-driven solutions leveraging machine learning and artificial intelligence. You will ...

Themis

Machine Learning Engineer

Mississauga, ON

CA$85K - CA$135K/yr

Machine Learning Engineer About Themis Intelligence Themis Intelligence builds the Utility Knowledge Base (UKB) and Human-Guided Intelligence (HGI) platforms, redefining how utilities operate. Our ...

Machina Labs

Machine Learning Engineer

Chatsworth, ON

CA$160K - CA$190K/yr

We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of what's possible in smart manufacturing. In this role, you will design, build, train, and deploy ...

Td

Machine Learning Engineer

Toronto, ON · On-site

CA$120K - CA$250K/yr

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 ...

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Trainee Machine Learning Engineer information

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 at large tech companies can earn $500,000 or more annually. Compensation may include base salary, bonuses, and stock options, especially in competitive markets or executive-level roles.

Can I get into AI with no experience?

The Trainee Machine Learning Engineer role typically requires some foundational knowledge of programming, mathematics, and data analysis. While prior experience is not always mandatory, gaining skills in Python, machine learning frameworks, and completing relevant courses or certifications can improve your chances of entering the field.

Can I learn ML in 3 months?

A Trainee Machine Learning Engineer can acquire foundational knowledge in three months by focusing on core concepts such as algorithms, programming in Python, and data handling. However, mastering advanced topics and gaining practical experience typically requires longer, ongoing learning and project work.

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

AspectTrainee Machine Learning EngineerJunior Data Scientist
Required CredentialsBasic programming, introductory ML knowledge, possibly a degree in CS or related fieldDegree in Data Science, Statistics, or related field; some programming experience
Work EnvironmentInternship or entry-level role in tech or AI companies, labs, or startupsEntry-level position in data teams across various industries
Employer & Industry UsageTech companies, AI startups, research labsFinance, healthcare, e-commerce, and tech firms

While both roles are entry-level and involve working with data, a Trainee Machine Learning Engineer focuses more on developing and deploying machine learning models, whereas a Junior Data Scientist emphasizes data analysis, visualization, and insights. The roles often overlap, but the Trainee ML Engineer is more specialized in ML algorithms and model deployment.

Which 5 jobs will survive AI?

For a Trainee Machine Learning Engineer, roles that require complex problem-solving, creativity, and human judgment—such as AI ethics specialists, data scientists, AI product managers, research scientists, and domain-specific consultants—are likely to persist despite advances in AI. These jobs often involve understanding nuanced contexts, strategic decision-making, and interpersonal skills that are difficult for AI to replicate. Developing expertise in areas like critical thinking, communication, and specialized knowledge will enhance job security in the evolving AI landscape.
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 Trainee Machine Learning Engineer jobs in Ontario? For Trainee Machine Learning Engineer jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Trainee Machine Learning Engineer jobs in Ontario look for? The top searched job categories for Trainee Machine Learning Engineer jobs in Ontario are:
What cities in Ontario are hiring for Trainee Machine Learning Engineer jobs? Cities in Ontario with the most Trainee Machine Learning Engineer job openings:
Trainee Machine Learning Engineer jobs near you
Infographic showing various Trainee Machine Learning Engineer job openings in Ontario as of June 2026, with employment types broken down into 80% Full Time, 12% Part Time, 5% Temporary, and 3% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution.

Machine Learning Engineer

Quincus

Toronto, ON • On-site

Full-time

Posted 7 days ago

Job description

"Make every logistics journey your best one yet"

The Company.
Founded in 2014, Quincus is a B2B supply chain operating SaaS platform headquartered in Singapore. We solve today's global supply chain challenges with groundbreaking technology. Using AI and machine learning, we have digitized and optimized the logistics process while giving customers full transparency into their supply chain. 
 
Quincus was founded by two visionary entrepreneurs who possess more than a decade of experience in tech. Chief Product Officer Katherina-Olivia Lacey is leading a tech revolution in this space while empowering women in the supply chain industry. Jonathan E. Savoir, Chief Executive Officer, appeared on Forbes' 30 Under 30 Asia List in 2020, and also serves on the boards of several startups.  

Overview.
Quincus Research is building the next generation of intelligent systems for all Quincus products. To achieve this, we're working on projects that utilize the latest computer science techniques developed by skilled software engineers and research scientists. Quincus Research teams collaborate closely with other teams across Quincus, maintaining the flexibility and versatility required to adapt new projects and focuses that meet the demands of the world's fast-paced business needs. 

Job Overview. 
We are looking for a highly motivated and experienced machine learning engineer to join our team and help us develop and deploy deep learning and reinforcement learning algorithms at scale. As a machine learning engineer, you will be responsible for designing and implementing scalable systems for serving models, optimizing inference performance, and managing production workflows. 

Responsibilities: 
- Design and implement scalable systems for serving deep learning and reinforcement learning models.
- Optimize inference performance of deep learning and reinforcement learning models using techniques such as quantization, pruning, and distillation.
- Utilize GPU computing to accelerate model training and inference.
- Develop and deploy production workflows for training and serving machine learning models.
- Collaborate with data scientists and software engineers to design and implement machine learning systems.
- Monitor and improve the performance of machine learning models in production.
- Stay up-to-date with the latest research and techniques in deep learning and reinforcement learning. 
 
Qualifications:
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
- 3+ years of experience in software engineering or machine learning engineering.
- Strong programming skills in Python (C++ or Java a plus)
- Experience with deep learning frameworks such as TensorFlow or PyTorch.
- Experience with GPU programming using CUDA, OpenCL, or similar libraries.
- Experience with distributed systems and cloud computing platforms such as Kubernetes, Docker, GCP, and AWS. 

Preferred Qualifications: 
- Ph.D. in Computer Science, Electrical Engineering, or a related field.
- 5+ years of experience in software engineering or machine learning engineering.
- Experience with reinforcement learning algorithms and frameworks.
- Experience with production deployment of machine learning models and implementation of APIs for big data.
- Strong understanding of computer architecture and performance optimization.
- Strong communication and collaboration skills. 

If you are passionate about developing and deploying machine learning algorithms at scale, and want to join a dynamic team working on cutting-edge technology, we encourage you to apply for this position.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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