What Is a Machine Learning Engineer? (+ How to Get Started)

Machine learning is a branch of AI that predicts and adapts outcomes as it receives more data. As more data becomes publicly available, corporations of all sizes are beginning to unlock the potential for machine learning to improve operations and outcomes. 

Statistics Canada employs machine learning for several exciting projects, including predicting crop yield, improving medical examination data, and extracting financial information. Professionals with machine learning skills, such as machine learning engineers, make these projects possible [6]. 

While a machine learning engineer isn't an entry-level position, the path to becoming one can be exciting and rewarding.

What is machine learning?

Machine learning is a part of the computer science field specifically concerned with AI. It uses algorithms to interpret data in a way that replicates how humans learn. The goal is for the machine to improve its learning accuracy and provide the user with data based on that learning [1].

Machine learning includes everything from video surveillance to facial recognition on your smartphone. However, customer-facing businesses also use it to understand consumers' patterns and preferences and design direct marketing or ad campaigns. 

Social media platforms like Facebook use machine learning to target users with advertisements based on their preferences, likes, and posts. Similarly, shopping websites like Amazon use algorithms to suggest items to buy based on a customer's purchases and viewing history [2].

Learn more about the real-world applications of machine learning in this lecture from Stanford and DeepLearning.AI's Machine Learning Specialization:

What machine learning engineers do

Machine learning engineers act as critical members of the data science team. Their tasks involve researching, building, and designing the AI responsible for machine learning and maintaining and improving existing AI systems. 

Often, a machine learning engineer will also serve as a critical communicator between other data science team members, working directly with the data scientists who develop the models for building AI systems and the people who construct and run them.

While job responsibilities for machine learning engineers will differ, they often include:

• Implementing machine learning algorithms

• Running AI systems experiments and tests

• Designing and developing machine learning systems

• Performing statistical analyses 

Other job opportunities

Over the past few decades, the computer science field has continued to grow. The in-demand nature of these skills and high technical proficiency requirements leads to higher-than-average pay for professionals in this industry [7]. As of July 2024, Glassdoor reports the median annual salary for computer programmers in Canada is $80,693 [3]. Advanced certifications and skills, such as machine learning, may increase earning potential [8]. 

Job outlook

The tech sector in Canada is booming, with 22.4 per cent growth expected between 2021 and 2024. With such high growth predicted for the tech sector, there is likely to be high demand for professionals with skills in emerging technology, such as machine learning and artificial intelligence [4]. In fact, the Canadian Job Bank reports there may be a shortage of professionals to fill AI roles, which could further increase the value of these skills [9].

Machine learning engineer salary

Like many high-level technology and computer science jobs, machine learning engineers earn salaries significantly above the national average—often near six figures. In fact, as of July 2024, the average base salary for a machine learning engineer is $113,084, according to Glassdoor [5].

How to become a machine learning engineer

It's possible to work your way up to becoming a machine learning engineer. You'll need to take three essential steps to become a machine learning engineer.

1. Earn a bachelor's degree in computer science or a related field.

Because machine learning is part of the computer science field, a strong background in computer programming, data science, statistics, and mathematics is essential for success. Most machine learning engineering jobs will require at least a bachelor's degree, so beginning a course of study in computer science or a closely related field, such as statistics, is a good first step [10].

2. Gain entry-level work experience.

Once you have earned a bachelor’s degree, the next step is to start working in the data science field to gain machine learning or AI experience. Some entry-level positions that will lead to a machine learning career include: 

• Computer engineer

• Data scientist

• Software developer

• Software engineer

3. Earn an advanced degree

You can work in machine learning and AI with just a bachelor's degree, pursuing a master's degree or Ph.D. in computer science, data science, computational statistics, or software engineering can help you learn the more complex tasks machine learning engineers encounter. It will also give you leverage as you apply for jobs, especially if you have bolstered your studies with plenty of internships or apprenticeships [10].

4. Maintain current skills and certification

As technology continues to develop, machine learning engineers need to commit  to continue learning new software, techniques, and skills in the field. Ongoing professional development can help you stay effective in your role and boost your resume to open new professional opportunities [10].

5. Build your machine learning expertise.

While working in a related role, you can build specialized experience to prepare you for machine learning engineering. Consider working on machine learning projects to practice essential skills or earning relevant certifications. Here are a few recommendations for getting started:

• IBM Machine Learning Professional Certificate

• IBM AI Engineering Professional Certificate

• Build a Machine Learning Web App with Streamlit and Python (Guided Project)

• Unsupervised Machine Learning for Customer Market Segmentation (Guided Project)

• Cervical Cancer Risk Prediction Using Machine Learning (Guided Project)

Get started in machine learning today

Artificial intelligence and machine learning are growing branches of computer and data science. Becoming a machine learning engineer requires years of experience and education, but you can start today.

Build your knowledge of software development, learn various programming languages, and work towards an initial bachelor's degree. A variety of certificates and even computer science degree pathways on Coursera can help prepare you for an exciting career in the machine learning field.

The machine learning specialization from Stanford University and DeepLearning.AI is another great introduction to machine learning, in which you'll learn all you need to know about supervised and unsupervised learning.