Salary for Mid-level / Intermediate Machine Learning Engineer in Canada during 2024

Salary details

The average mid-level / intermediate Machine Learning Engineer salary lies between USD 117,000 and USD 180,000 in Canada. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.

Region represents the primary country of residence of an employee during the year (or residence for tax purposes). All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Dec. 1, 2024

Top 20 Job Tags for Mid-level / Intermediate Machine Learning Engineer roles

The three most common job tag items assiciated with mid-level / intermediate Machine Learning Engineer job listings are Machine Learning, Python and Engineering. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:

Machine Learning | 997 jobs Python | 825 jobs Engineering | 747 jobs ML models | 619 jobs Computer Science | 545 jobs PyTorch | 501 jobs TensorFlow | 439 jobs Pipelines | 423 jobs AWS | 383 jobs Research | 375 jobs Deep Learning | 322 jobs Statistics | 317 jobs NLP | 313 jobs SQL | 280 jobs Architecture | 265 jobs Testing | 248 jobs MLOps | 247 jobs LLMs | 247 jobs Scikit-learn | 231 jobs Kubernetes | 218 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate Machine Learning Engineer roles

The three most common job benefits and perks assiciated with mid-level / intermediate Machine Learning Engineer job listings are Career development, Health care and Equity / stock options. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:

Career development | 818 jobs Health care | 428 jobs Equity / stock options | 332 jobs Flex hours | 298 jobs Competitive pay | 251 jobs Flex vacation | 236 jobs Startup environment | 218 jobs Insurance | 191 jobs Parental leave | 186 jobs Medical leave | 169 jobs Team events | 163 jobs Salary bonus | 128 jobs 401(k) matching | 108 jobs Wellness | 88 jobs Conferences | 61 jobs Relocation support | 53 jobs Home office stipend | 46 jobs Flexible spending account | 44 jobs Fitness / gym | 40 jobs Unlimited paid time off | 38 jobs

Salary Composition for a Mid-level Machine Learning Engineer

The salary for a mid-level Machine Learning Engineer in Canada typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually forms the largest part of the total compensation package. Performance bonuses can vary significantly depending on the company's profitability and individual performance metrics. Additional remuneration might include stock options, especially in tech companies or startups, and benefits like health insurance, retirement plans, and professional development allowances. The composition can vary by region, with tech hubs like Toronto and Vancouver often offering higher base salaries and more comprehensive benefits. Industry also plays a role; for instance, finance and tech sectors might offer more lucrative bonuses compared to academia or public sector roles. Company size can influence the package as well, with larger companies often providing more structured and predictable compensation packages, while startups might offer more equity-based incentives.

Steps to Increase Salary from a Mid-level Position

To increase your salary from a mid-level Machine Learning Engineer position, consider the following strategies:

• Skill Enhancement: Continuously update your skills with the latest technologies and methodologies in AI/ML. Specializing in high-demand areas like deep learning, natural language processing, or computer vision can make you more valuable.
• Advanced Education: Pursuing a master's or Ph.D. in a relevant field can open doors to higher-level positions and salary brackets.
• Leadership Roles: Transitioning into roles that combine technical expertise with leadership, such as a team lead or project manager, can lead to salary increases.
• Networking and Industry Engagement: Actively participating in industry conferences, workshops, and meetups can lead to new opportunities and salary negotiations.
• Company Change: Sometimes, moving to a different company, especially one in a high-demand sector or a larger organization, can result in a significant salary bump.

Educational Requirements

Most mid-level Machine Learning Engineer positions require at least a bachelor's degree in computer science, data science, mathematics, statistics, or a related field. However, many employers prefer candidates with a master's degree or higher, especially for roles that involve complex problem-solving and advanced algorithm development. A strong foundation in mathematics, particularly in linear algebra, calculus, and probability, is essential. Additionally, coursework or experience in programming languages such as Python, R, or Java, and familiarity with machine learning frameworks like TensorFlow or PyTorch, is often required.

Helpful Certifications

While not always mandatory, certain certifications can enhance your profile and demonstrate your commitment to the field. Some valuable certifications include:

• Google Professional Machine Learning Engineer: Validates your ability to design, build, and productionize ML models on Google Cloud.
• AWS Certified Machine Learning – Specialty: Demonstrates expertise in building, training, tuning, and deploying ML models on AWS.
• Microsoft Certified: Azure AI Engineer Associate: Focuses on using Azure services to build and deploy AI solutions.
• TensorFlow Developer Certificate: Shows proficiency in using TensorFlow to build and train models.

Experience Requirements

Typically, a mid-level Machine Learning Engineer is expected to have 3-5 years of relevant experience. This experience should include hands-on work with machine learning models, data analysis, and software development. Experience in deploying models to production and working in cross-functional teams is also highly valued. Practical experience with data preprocessing, feature engineering, and model evaluation is crucial.

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