
How to become a machine learning engineer
Build the models that power AI at Swinburne. Learn about the courses, skills, tools, and career steps you need to become a machine learning engineer.
Ever wondered how your favourite apps seem to know exactly what you need, how virtual assistants understand your voice, or how self-driving cars make split-second decisions? That’s machine learning in action, and as a machine learning engineer, you’ll be the brain behind the tech.
Machine learning engineers are in high demand globally, so if you love working with data, coding up clever solutions, and want to be at the forefront of Artificial Intelligence (AI) innovation, this could be the perfect career for you. Machine learning is the driving force behind artificial intelligence, shaping automation, predictive analytics, and intelligent decision-making across industries from healthcare and finance to gaming and cybersecurity.
At Swinburne, we don’t just teach theory
You’ll get hands-on with the latest tools, techniques, and technologies that power real-world AI applications. From neural networks and deep learning to data modelling and natural language processing, you’ll build the practical skills employers are looking for. With industry-connected learning, you’ll work on real projects and graduate with experience that sets you apart.
Ready to train your own career model? Explore the study options and pathways that will get you there.
3 steps to become a machine learning engineer
Becoming a machine learning engineer takes three steps: earning qualifications, gaining industry experience, and building a professional network.
With Swinburne’s UniLink diplomas to masters, there’s a course for every stage, whether you’re starting out or building on existing skills.
1. Gain a qualification
Wherever you are in your career, Swinburne has a flexible pathway into AI and machine learning.
Build your foundation with a Diploma of Information Technology (UniLink) or Diploma of Engineering (Unilink).
Deepen your knowledge with a Bachelor of Computer Science or Bachelor of Engineering (Honours).
Advance your expertise with a Master of Data Science.
With industry-connected learning and hands-on experience, Swinburne’s programs give you the skills to thrive in one of the most exciting and fast-evolving industries today.
Bachelor degrees
Bachelor of Computer Science (Majoring in Artificial Intelligence)
This course is for you if:
- you’re passionate about coding and AI development.
What you'll gain:
- Develop skills in programming, computer systems, and architecture.
- Explore AI-focused topics like Introduction to Artificial Intelligence, Applied Machine Learning, Intelligence Systems.
- Apply your skills in real-world projects through Swinburne’s Work Integrated Learning (WIL) program.
- Gain industry experience with an optional 6- or 12-month work placement.
- Graduate with a qualification accredited by the Australian Computer Society.
Want guaranteed industry experience?
Enrol in the Bachelor of Computer Science Professional for a guaranteed paid 12-month work placement, giving you a head start in the industry.
Bachelor of Engineering (Honours) (Major in Software)
This course is for you if:
- you want to combine AI with software engineering to solve real-world problems.
What you'll gain:
- Develop skills in engineering, programming, and statistical modelling.
- Study key topics like Artificial Intelligence for Engineering, Data Structures and Patterns, Introduction to Artificial Intelligence.
- Get hands-on with WIL-based projects every semester.
- Gain industry experience with an optional 6- or 12-month placement.
- Graduate with confidence—your degree is accredited by Engineers Australia.
Want guaranteed industry experience?
Choose the Bachelor of Engineering (Professional).
Bachelor of Engineering/Bachelor of Computer Science (Double Degree)
This course is for you if:
- You want to build advanced skills in engineering and computer science.
What you'll gain:
Combine AI, engineering and programming in a multidisciplinary, double degree
Graduate with dual accreditation from Engineers Australia and the Australian Computer Society.
Postgraduate study
Master of Data Science
This course is for you if:
- You’re ready to take your career to the next level in AI and data science.
What you'll gain:
Master skills in big data, cloud computing, and AI-driven solutions
Gain expertise in Machine Learning, Cloud Engineering, Big Data, Data Management for the Big Data Age, and Data Visualisation.
Work on real-world tech projects both independently and in teams
Graduate with a qualification accredited by the Australian Computer Society.
Pathway courses
Unilink Diploma of IT (Computer Science Stream)
This course is for you if:
- You want a direct pathway into a computer science degree.
What you'll gain:
Build a strong foundation in IT with a focus on computer science
Fast-track into the second year of the Bachelor of Data Science or Bachelor of Computer Science with guaranteed entry.
UniLink Diploma of Engineering
This course is for you if:
- You want a pathway into an engineering degree.
What you'll gain:
Kick-start your engineering journey with a strong technical foundation.
Secure your place in second year of the Bachelor of Engineering (Honours) with guaranteed entry.
2. Gain experience
At Swinburne, you won’t just learn the theory-you’ll apply it from day one. Every Swinburne bachelor degree includes a Work Integrated Learning (WIL) guarantee, ensuring you graduate with hands-on industry experience.
Industry-based learning: Work on real-world projects throughout your studies.
Take your work experience further: Apply for an optional 6- or 12-month work placement or internship to expand your expertise and professional network, or enrol in a professional bachelor degree for a guaranteed 12 month work placement.
With decades of strong industry connections, Swinburne gives you access to a wide professional network to help launch your career.
3. Build a professional network
Studying at Swinburne isn’t just about gaining knowledge—it’s about connecting with industry professionals and like-minded peers.
Join student-led clubs and communities
Swinburne Computer Science Club – collaborate on AI and coding challenges.
Cyber Security Club – dive into ethical hacking and cybersecurity trends.
Swinburne Engineering Society – tackle engineering projects and connect with experts.
Women in STEM – join a supportive community championing diversity in tech.
Get professional accreditation
Engineers Australia – start as an Engineers Australia student member and upgrade upon graduation.
Australian Computer Society – ACS membership provides access to career resources, certifications, and networking opportunities.
By the time you graduate, you’ll have a degree, hands-on experience, and a strong industry network to help you take the next step in your career.
Explore our machine learning and artificial intelligence courses
How long will it take to become a machine learning engineer?
Kickstart your career in machine learning with Swinburne. Choose from an entry-level diploma (8 months), a bachelor’s degree (3–4 years), or a masters (2 years). With full-time, part-time, and fast-track options, you can study at your own pace—even over summer or winter terms.
-
-
-
Postgraduate degrees
2 years full-time or part-time equivalent
Quick facts about machine learning engineering
Average salary
$160K+
Job growth
22% globally
Job satisfaction
4.1/5
Skills required to become a machine learning engineer
Technical skills:
programming languages: Python, R, Java
ML Frameworks & Libraries: TensorFlow, PyTorch, Keras
data processing & engineering
software engineering
data visualisation & technical documentation.
Mathematical foundations:
- linear algebra
- calculus
- statistics
- probability.
ML-specific knowledge:
- ML algorithms
- ML techniques
- model evaluation & MLOps.
Study machine learning at Swinburne
Courses and study pathways
Vocational courses that pathway into a degree.
Duration:
8 months full-time or part-time equivelant
Entry requirements:
VCE or relevant work experience
Future study pathways:
Successfully completing this course guarantees entry into the second year of the Bachelor of Computer Science or Bachelor of Engineering (Honours).
Recommended courses:
Gain real industry experience as part of your degree through placements, internships, or industry-linked projects.
Duration:
3-4 years full-time or part-time equivalant
Entry requirements:
Successful completion of VCE, a relevant UniLink diploma, or an approved tertiary qualification (completed or partially completed), including a diploma, advanced diploma, associate degree, or degree
Career outcomes:
- Machine learning engineer
- Software engineer
- Software modeller
Recommended courses:
Take your career to the next level with a Swinburne postgraduate qualification, designed to prepare you for senior technical and leadership roles in the evolving world of data and AI.
Duration:
2 years full-time or part-time equivalent
Entry requirements:
A completed bachelor’s degree in any discipline, a graduate certificate or diploma in data science from a recognised higher education institution, or completion of the Postgraduate Qualifying Program
Career outcomes:
machine learning engineer
data engineer
data architect
- statistician
Accreditation
Australian Computer Society
Recommended course:
Why study machine learning engineering at Swinburne?
-
Ranked in the Top 25 Young Universities worldwide
-
Guaranteed Work Integrated Learning in every bachelor degree
-
5-star student-to-teacher ratio for personalised support and learning
Frequently asked questions
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.
Machine Learning Engineers create the systems that allow computers to learn, adapt, and improve without direct programming. They develop and fine-tune algorithms, process large datasets, and design models that power everything from fraud detection and climate forecasting to robotics and autonomous systems.
Their work blends software engineering with data science, turning complex theories into real-world applications that drive innovation across industries like healthcare, finance, cybersecurity, and manufacturing.
Curious about studying machine learning at Swinburne?
Book a one-on-one consultation with our Future Student advisors.
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.
Unsure about your next step?
Chat with our future student advisors to explore your options.
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.
Ready to take the first step?
Explore your study options with our Future Student Advisors.
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.
- Weforum Future of Jobs Report 2025
- Nucamp Job Growth Australia AI related roles
- Refontelearning Salary Guide
- Weforum Future of Jobs report 2023
- Clicks.com.au Job Salary
- Job Satisfaction: Glassdoor career report 2024
- a) Swinburne Young University Rankings b) Student:teacher ratio: Good Universities Guide 2024
Start your journey in machine learning and shape the future of AI
Our course guide has the details you want.