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How to Become A Machine Learning Engineer

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What is a Machine Learning Engineer

A Machine Learning Engineer creates algorithms that let computers learn from data. They build systems that improve on their own and make predictions without being told what to look for. It is one of the most exciting and in-demand tech roles in Australia right now.

The work blends coding, data analysis, and problem-solving. Day to day, an engineer cleans and processes data, trains models, tests their performance, and ships them to real apps. Python, TensorFlow, and PyTorch are the most common tools used on the job.

No two days look the same in this role. A typical day might start with debugging a model and end with presenting findings to a product team. Working with data scientists and software developers is a regular part of the role.

Australia’s appetite for machine learning talent is growing fast. Jobs and Skills Australia flagged this as an emerging role with a big rise in advertised positions since 2018. This career offers real variety, strong pay, and the chance to work on technology that shapes everyday life.

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Career Outlook for Machine Learning Engineer

Machine Learning Engineers in Australia have great job prospects. Most are aged between 25 and 44, and 86% work full-time (Jobs and Skills Australia, 2022). The field draws people who love solving problems and working with data each day.

Pay is strong. The average salary is around AU$100,000 per year (PayScale, 2026). Entry-level roles start at around AU$62,000. Senior engineers can earn AU$140,000 or more. Demand is rising fast. Job ad numbers more than doubled between 2018 and 2022 (Jobs and Skills Australia, 2022).

The future looks bright for this career. Australia’s push into AI across health, finance, and government means more groups need skilled ML engineers. Those who build strong portfolios and keep up with new tools will find plenty of work ahead.

On this Article

Introduction

Average Salaries

Resources

Steps to become a Machine Learning Engineer

Step 1: Complete a Bachelor’s Degree in Computer Science, Data Science, or Maths

Enrol in a Bachelor of Computer Science, Bachelor of Data Science, or Bachelor of Science (Maths) at an Australian university. These AQF Level 7 degrees take 3 years full-time. They build your base in coding, logic, stats, and data structures. Look for a degree with Python units and intro machine learning content. These are standard entry-level needs for most roles.

Step 2: Build Your Core Technical Skills

During and after your degree, focus on Python and key ML tools including TensorFlow, PyTorch, and Scikit-learn. Practice with real datasets on platforms like Kaggle. Aim to finish at least two full projects covering data cleaning, model training, and testing. Cloud tools such as AWS SageMaker and Google Vertex AI are also worth learning early. Allow 6 to 12 months of steady practice to build a firm base.

Step 3: Complete Postgraduate Study in Data Science or Machine Learning

A Master of Data Science or Master of Artificial Intelligence (AQF Level 9) deepens your skills. It opens the door to senior and specialist roles. These programs take 1.5 to 2 years full-time at most Australian universities. They cover deep learning, natural language processing, and model deployment. Check current offerings at your chosen institution to confirm entry needs.

Step 4: Build a Portfolio of Machine Learning Projects

Create a GitHub portfolio with end-to-end ML projects, from raw data to a live model. Include at least three projects across different types, such as sorting, prediction, and language tasks. Record your methods clearly. A strong portfolio often matters more than your degree when you apply for early-career roles.

Step 5: Join the ACS and Apply for Entry-Level Roles

Become a member of the Australian Computer Society (ACS). Membership gives you access to events, networks, and a known credential for career growth. From there, apply for junior or associate Machine Learning Engineer roles in tech firms, banks, health groups, or government. Many engineers enter via data analyst or software roles and move across within one to two years.

What does a Machine Learning Engineer do?

A Machine Learning Engineer’s day is a mix of data work, model building, and team catch-ups. They start by going over project goals with data scientists and setting out what the model needs to do. From there, they clean data, write and test code, and check how well their models run. They also watch live models in use, fix issues, and lift accuracy as new data comes in. Team check-ins with product and engineering groups keep the work on track. It is a hands-on role where problem-solving and curiosity make a real difference each day.

Tasks

Machine Learning Engineers sit at the heart of Australia’s data revolution. They turn raw data into models that power real products, from banking fraud alerts to personalised health tools. The role is highly technical but also depends on creative thinking and good teamwork.

  • Data collection: gathering and cleaning data from multiple sources so it is ready for analysis
  • Model development: building machine learning models to solve specific problems or improve processes
  • Algorithm selection: choosing the best algorithm for the data type and business goal
  • Performance evaluation: testing and validating models to check their accuracy and reliability
  • Collaboration: working with data scientists, developers, and stakeholders to ship ML solutions
  • Continuous learning: keeping up with new tools, research, and best practices in AI and ML
  • Documentation: writing clear records of models and methods for transparency and future use
  • Deployment: launching machine learning models into production for real-world use

Skills for Success

To become a Machine Learning Engineer, strong Python skills are a must. Most job ads ask for experience with TensorFlow or PyTorch. A solid understanding of statistics and data structures is also expected. Being comfortable with large datasets and SQL adds even more value.

Soft skills matter just as much as technical ones. Engineers who can break down complex ideas for non-technical colleagues stand out. Curiosity and a love of problem-solving go a long way. A willingness to keep learning is what helps people thrive in this fast-moving field.

Skills & Attributes

  • Programming in Python, with knowledge of R or Java as a secondary language
  • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn
  • Understanding of algorithms, data structures, and software design
  • Data wrangling and analysis using tools such as Pandas and NumPy
  • Statistical analysis and probability concepts
  • Data visualisation using tools such as Matplotlib or Seaborn
  • Working with large databases and SQL
  • Version control and software development practices using Git
  • Analytical thinking and problem-solving under real-world constraints
  • Clear communication of technical results to non-technical audiences
  • Teamwork and collaboration across data science and engineering teams
  • Experience with cloud platforms such as AWS, Google Cloud, or Azure
  • Awareness of ethical and responsible AI practices

Average Salaries

Machine Learning Engineers in Australia earn an average of AU$100,000 per year (PayScale, 2026). Entry-level roles start at around AU$62,000. Senior engineers with specialist skills can earn up to AU$147,000.

min $62 K

max $147 K

av $100 K

About the author

Laura Atkinson - Course Finder Groups
Laura Atkinson

Laura Atkinson is an Account Management and SEM specialist at Course Finder Group with six years' experience in the Australian education sector. She works day to day with universities, TAFEs and independent training providers, which gives her a close view of how courses map to real career outcomes. She writes practical guidance on career pathways, choosing the right qualification and what to expect when moving into a new field.