Salary for Mid-level / Intermediate AI Engineer in Canada during 2024
💰 The median Salary for Mid-level / Intermediate AI Engineer in Canada during 2024 is USD 134,000
✏️ This salary info is based on 10 individual salaries reported during 2024
Salary details
The average mid-level / intermediate AI Engineer salary lies between USD 120,000 and USD 134,000 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.
- Job title
- AI Engineer
- Experience
- Mid-level / Intermediate
- Region
- Canada
- Salary year
- 2024
- Sample size
- 10
- Top 10%
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- Top 25%
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- Median
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- Bottom 25%
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- Bottom 10%
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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:Top 20 Job Tags for Mid-level / Intermediate AI Engineer roles
The three most common job tag items assiciated with mid-level / intermediate AI 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 | 241 jobs Python | 239 jobs Engineering | 229 jobs Computer Science | 167 jobs LLMs | 149 jobs NLP | 141 jobs Generative AI | 127 jobs Research | 124 jobs PyTorch | 123 jobs AWS | 117 jobs TensorFlow | 116 jobs Azure | 109 jobs Architecture | 96 jobs Deep Learning | 90 jobs Pipelines | 85 jobs ML models | 79 jobs Java | 75 jobs GCP | 71 jobs APIs | 71 jobs Agile | 70 jobsTop 20 Job Perks/Benefits for Mid-level / Intermediate AI Engineer roles
The three most common job benefits and perks assiciated with mid-level / intermediate AI Engineer job listings are Career development, Health care and Flex hours. 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 | 212 jobs Health care | 109 jobs Flex hours | 91 jobs Equity / stock options | 67 jobs Competitive pay | 66 jobs Startup environment | 52 jobs Parental leave | 51 jobs Flex vacation | 49 jobs Insurance | 43 jobs Team events | 41 jobs Medical leave | 41 jobs Wellness | 38 jobs 401(k) matching | 36 jobs Salary bonus | 21 jobs Fitness / gym | 18 jobs Home office stipend | 18 jobs Transparency | 16 jobs Conferences | 15 jobs Unlimited paid time off | 11 jobs Relocation support | 10 jobsSalary Composition for Mid-level AI Engineer Roles
In Canada, the salary composition for a mid-level AI Engineer typically includes a base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually constitutes the majority of the total compensation package. Performance bonuses can vary significantly depending on the company's success and individual performance, often ranging from 10% to 20% of the base salary. Additional remuneration might include stock options, especially in tech companies or startups, and comprehensive benefits packages that cover health insurance, retirement plans, and other perks. The composition can vary based on the region, with tech hubs like Toronto and Vancouver offering higher salaries. Industry also plays a role; for instance, AI roles in finance or healthcare might offer different compensation structures compared to those in retail or manufacturing. Company size can influence the package as well, with larger companies often providing more structured and potentially higher bonuses and benefits.
Steps to Increase Salary from a Mid-level Position
To increase your salary from a mid-level AI Engineer position, consider the following strategies:
- Skill Enhancement: Continuously update your skills with the latest AI/ML technologies and tools. 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 degree or Ph.D. in AI, machine learning, or data science can open doors to higher-paying roles.
- Leadership Roles: Transitioning into a leadership or managerial role can significantly boost your salary. This might involve leading a team of engineers or managing AI projects.
- Networking: Building a strong professional network can lead to opportunities in higher-paying companies or industries.
- Certifications: Obtaining advanced certifications can demonstrate your expertise and commitment to the field, potentially leading to salary increases.
Educational Requirements for Mid-level AI Engineer Roles
Most mid-level AI Engineer positions require at least a bachelor's degree in computer science, engineering, mathematics, or a related field. However, many employers prefer candidates with a master's degree in AI, machine learning, or data science. A strong foundation in mathematics, statistics, and programming is essential. Courses in data structures, algorithms, and software engineering are also highly beneficial. Some roles might require knowledge of specific AI frameworks or tools, which can be acquired through formal education or self-study.
Helpful Certifications for AI Engineers
While not always mandatory, certain certifications can enhance your profile and demonstrate your expertise to potential employers. Some valuable certifications include:
- Google Professional Machine Learning Engineer
- AWS Certified Machine Learning – Specialty
- Microsoft Certified: Azure AI Engineer Associate
- IBM AI Engineering Professional Certificate
These certifications can validate your skills in specific platforms and tools, making you more competitive in the job market.
Experience Requirements for Mid-level AI Engineer Roles
Typically, mid-level AI Engineer roles require 3 to 5 years of experience in AI, machine learning, or data science. This experience should include hands-on work with machine learning models, data analysis, and software development. Experience in deploying AI solutions in production environments is highly valued. Additionally, familiarity with popular AI frameworks like TensorFlow, PyTorch, or Scikit-learn is often required. Experience in a specific industry, such as finance, healthcare, or technology, can also be advantageous.
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