Canada is a burgeoning hub for artificial intelligence and machine learning innovation. With its strong tech ecosystem and government support for AI research, Canada offers exciting opportunities for Machine Learning Engineers. If you have a passion for building intelligent systems, a strong foundation in math and programming, and a knack for solving complex problems, consider exploring Machine Learning Engineer roles in Canada.
Roles and Responsibilities of Machine Learning Engineers:
Aspiring Machine Learning Engineers in Canada should be prepared to undertake a range of responsibilities, including:
- Algorithm Development: Designing, implementing, and optimizing machine learning algorithms and models to solve complex problems across various domains such as finance, healthcare, e-commerce, and more.
- Data Preparation: Collecting, preprocessing, and cleaning large datasets to ensure they are suitable for training and evaluation of machine learning models. This includes feature engineering, data augmentation, and handling missing values.
- Model Training and Evaluation: Utilizing machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, or R to train and evaluate models using appropriate metrics. This involves experimenting with different algorithms and hyperparameters to improve model performance.
- Deployment: Deploying machine learning models into production environments, integrating them with existing systems, and ensuring scalability, reliability, and performance. This may involve containerization, microservices architecture, and cloud computing platforms.
- Monitoring and Maintenance: Monitoring the performance of deployed models, detecting anomalies, and retraining models as necessary to adapt to changing data distributions and business requirements. Ensuring the robustness and stability of machine learning systems over time.
- Collaboration: Collaborating with cross-functional teams including data scientists, software engineers, product managers, and business stakeholders to understand requirements, prioritize tasks, and deliver solutions that meet business objectives.
- Continuous Learning: Staying abreast of the latest advancements in machine learning research and techniques, attending conferences, workshops, and online courses, and continuously improving skills and knowledge in this rapidly evolving field.
Companies Hiring for Machine Learning Engineer Jobs in Canada
Canada boasts a diverse landscape of companies actively seeking Machine Learning Engineers. Here are some of the notable examples:
Shopify
Requirements:
- Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field.
- Deep understanding of machine learning algorithms, statistical modeling, and optimization techniques.
- Proficiency in Python, PyTorch, TensorFlow, or similar deep learning frameworks.
- Experience in building and deploying machine learning models in a production environment.
Apply Here: Careers
RBC (Royal Bank of Canada)
Requirements:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a quantitative field.
- Solid foundation in machine learning concepts (supervised/unsupervised learning, reinforcement learning, NLP).
- Proficiency in Python or R, and experience with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Ability to collaborate with data scientists, software engineers, and business stakeholders.
Apply Here: Careers
TD Bank Group
Requirements:
- Master’s or PhD in Computer Science, Mathematics, or a related field with a specialization in machine learning.
- Experience applying machine learning to solve real-world problems in the financial industry.
- Strong programming skills in Python and proficiency with machine learning tools and frameworks.
- Ability to communicate complex technical concepts to non-technical stakeholders.
Apply Here: Careers
OpenText
Requirements:
- Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
- Experience with natural language processing (NLP), text analytics, and deep learning for text-based applications.
- Proficiency with NLP libraries and frameworks like NLTK, SpaCy, or TensorFlow.
- Knowledge of large-scale data processing and cloud technologies.
Apply Here: Careers
Element AI
Requirements:
- Strong academic background in machine learning, with publications in top-tier conferences or journals.
- Deep expertise in developing and deploying large-scale machine learning systems, including deep learning models.
- Excellent communication skills and a track record of collaborating effectively in research and development environments.
Apply Here: Careers
Uber
Requirements:
- Experience in computer vision, deep learning, and perception techniques for autonomous driving applications.
- Proficiency in C++, Python, and relevant machine learning frameworks (TensorFlow, PyTorch).
- A strong understanding of sensor fusion and lidar/radar data processing.
- Passion for developing cutting-edge self-driving technologies.
Apply Here: Careers
Amazon
Requirements:
- Graduate degree (Master’s or PhD) in Computer Science, Machine Learning, Statistics, or a related field.
- Proficiency in statistical modeling, machine learning algorithms, and deep learning techniques.
- Experience in building, deploying, and scaling machine learning models in production.
- Strong software development skills and fluency in Python, Java, or Scala.
Apply Here: Careers
DeepMind
Requirements:
- PhD in machine learning, computer science, mathematics, or a related quantitative field.
- Strong research background in deep learning, reinforcement learning, or other machine learning areas.
- Exceptional programming skills in Python, TensorFlow, or PyTorch.
- A desire to push the boundaries of artificial intelligence research.
Apply Here: Careers
Microsoft
Requirements:
- Master’s or PhD in Computer Science, Electrical Engineering, Statistics, or a related discipline with a focus on machine learning.
- Expertise in areas such as natural language processing, computer vision, or reinforcement learning.
- Proficiency with Python, C++, or other relevant programming languages.
- Ability to work collaboratively in a cross-functional team environment.
Apply Here: Careers
Borealis AI
Requirements:
- Master’s or PhD in Computer Science, Mathematics, or related field with a specialization in machine learning.
- Proven experience in research and development of machine learning solutions for financial applications.
- Understanding of financial markets, risk modeling, and fraud detection.
- Strong problem-solving and analytical skills with the ability to translate complex data into actionable insights.
Apply Here: Careers
Northrop Grumman
Requirements:
- Background in developing machine learning and artificial intelligence solutions for defense or aerospace applications.
- Expertise in computer vision, sensor fusion, predictive analytics, or natural language processing.
- Experience working with large-scale datasets and distributed computing systems.
- Ability to work in a collaborative environment and adhere to security protocols.
Apply Here: Careers
MDA
Requirements:
- Experience in applying machine learning and AI techniques to satellite imagery, geospatial data analysis, or remote sensing.
- Proficiency in Python, relevant machine learning libraries, and geospatial analysis tools.
- Knowledge of GIS (Geographic Information Systems) and image processing techniques.
Apply Here: Careers
Cineplex Digital Media
Requirements:
- Experience in building machine learning-driven recommendation systems, personalization algorithms, or audience segmentation models.
- Solid foundation in machine learning techniques and statistical modeling.
- Proficiency in Python, R, or other relevant data science languages.
- Understanding of user behavior analytics and A/B testing methodologies.
Apply Here: Careers
Ritual
Requirements:
- Background in building machine learning models for food delivery, logistics optimization, or predictive analytics.
- Experience with large-scale data processing and real-time decision-making systems.
- Proficiency in Python, SQL, and machine learning frameworks.
- Strong problem-solving skills and the ability to translate business problems into technical solutions.
Apply Here: Careers
Magnet Forensics
Requirements:
- Experience in developing machine learning algorithms for digital forensics, cybersecurity, or threat detection.
- Understanding of malware analysis, network intrusion detection, and data security principles.
- Strong programming skills in Python or other relevant languages.
Apply Here: Careers
Job Portals
Here are popular job portals to aid your Machine Learning Engineer job search in Canada:
Salary of Machine Learning Engineers in Canada
Salaries for Machine Learning Engineers in Canada can vary based on factors such as experience, location, industry, and company size. Here’s a general guideline:
- Entry-level: CAD $80,000 – CAD $110,000 per year
- Mid-level: CAD $110,000 – CAD $150,000 per year
- Senior-level: CAD $150,000+ per year
Experience-Wise Salary Trend:
- Less than 1 year: CAD $70,000 – CAD $95,000 per year
- 1-3 years: CAD $90,000 – CAD $125,000 per year
- 4-6 years: CAD $120,000 – CAD $160,000 per year
- 7-9 years: CAD $150,000 – CAD $190,000 per year
- 10+ years: CAD $190,000+ per year
Machine Learning Engineer Jobs in Canada – FAQs
Are machine learning engineers in demand in Canada?
The demand for AI professionals in Canada is increasing due to effective collaboration between talent and innovation. AI applications are spread across industries, from finance and healthcare to manufacturing and transportation.
Do I need a Master’s or PhD to be a Machine Learning Engineer in Canada?
While a Master’s or PhD is advantageous, many companies value hands-on experience and a strong portfolio of projects.
How many hours does a machine learning engineer work?
On average, Machine Learning Engineers often work between 40 to 50 hours per week. The workload can fluctuate depending on the complexity of projects, approaching deadlines, and the data-driven nature of their role.
In conclusion, Machine Learning Engineers play a pivotal role in leveraging the power of artificial intelligence and machine learning to drive innovation and solve real-world problems in Canada. With a strong foundation in mathematics, programming, and machine learning concepts, coupled with hands-on experience and a passion for continuous learning, aspiring Machine Learning Engineers can embark on a rewarding career path in Canada’s thriving tech industry. By honing their skills, building a strong portfolio, and actively engaging with the vibrant machine learning community, individuals can position themselves for success and contribute to the advancement of AI-driven technologies in Canada and beyond.