How to become a machine learning engineer

Machine learning (ML) is how computers learn from experience. Instead of following a set of fixed instructions, they analyse data, recognise patterns, and make decisions—improving their accuracy over time, much like how we learn from practice.

Machine learning is the driving force behind artificial intelligence, powering everything from personalised recommendations to medical diagnostics and cutting-edge automation. As technology evolves, ML is transforming industries, solving complex problems, and shaping the future of innovation.

Technically, no—but having a degree makes a huge difference. While some people break into the field through self-study and experience, most employers look for candidates with strong skills in mathematics, programming, and data science. A structured qualification gives you the best foundation in key areas like algorithms, data structures, and applied machine learning, along with hands-on industry experience.

Many machine learning engineers have a bachelor degree in computer science or engineering. If you don’t have a degree yet, Swinburne offers pathway programs like the Diploma of Information Technolog (Unilink) or Diploma of Engineering (Unilink).  Completing one of these guarantees entry into the second year of the Bachelor of Computer Science or Bachelor of Engineering (Honours), helping you fast-track your way into the field.

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Like any specialised field, machine learning comes with its challenges—but that’s what makes it exciting. It combines computer science, engineering, and data science, so you’ll develop skills in mathematics, programming, and data analysis along the way.

The good news? You don’t have to be an expert from day one. Swinburne offers courses for all levels, whether you’re just starting out or looking to deepen your expertise. From Unilink Diplomas to postgraduate master’s degrees, you’ll get the support, hands-on experience, and industry connections to build your confidence and skills step by step.

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Machine learning engineers and data engineers both work with data, but they focus on different parts of the process.

Machine learning engineers design and develop intelligent systems that learn from data. They build and fine-tune ML models, train algorithms, and optimise performance to solve business problems, whether that’s predicting trends, automating tasks, or improving decision-making.

Data engineers create the foundation that makes all of this possible. They build and maintain the pipelines that move and process massive amounts of data, ensuring it’s clean, accessible, and structured for machine learning models to use effectively.

Think of it this way: data engineers set the stage, and machine learning engineers bring the models to life. Both roles are crucial in AI and data science, and choosing the right path depends on your interests and career goals. 

Not sure which one’s right for you?

Chat with our future student advisors to explore your options and find the best course to get you there.