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How to Become a Machine Learning Engineer: Australian Careers in Artificial Intelligence AI

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What is a Machine Learning Engineer?

What will I do?

What skills do I need?

Resources

What is a Machine Learning Engineer?

A Machine Learning Engineer builds systems that let computers learn from data. These engineers create tools that spot patterns, make predictions, and get better over time. They work in healthcare, finance, retail, and government.

Machine Learning Engineers blend software skills with data know-how. They work with data scientists to clean datasets, pick models, and tune them for use in live systems. The role calls for strong coding skills, data thinking, and clear communication.

The field moves fast. New tools and methods appear often, so learning on the job never stops. Engineers keep up through self-study, industry events, and groups like the Australian Computer Society (ACS).

Demand for Machine Learning Engineers in Australia is growing. Jobs and Skills Australia tracks this as an emerging role. Job ads in this field more than doubled between 2018 and 2022 (Jobs and Skills Australia, 2022).

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Career snapshots For Machine Learning Engineers

Machine Learning Engineers in Australia work full-time in the vast majority of cases. The role is concentrated in major cities, particularly Sydney and Melbourne.

Based on the 2021 Census and Lightcast data, 86% of Machine Learning Engineers worked full-time. Around 87% were aged between 25 and 44. Women made up 15% of the workforce, reflecting an ongoing diversity gap in the field (Jobs and Skills Australia, 2022).

Online job ads for Machine Learning Engineers more than doubled between 2018 and 2022. They grew from 121 to 298 per year (Jobs and Skills Australia, 2022). The average salary is around AU$100,000 per year, with senior engineers earning above AU$140,000 (PayScale, 2026).

What will I do?

Machine Learning Engineers build systems that let computers learn from data. Their work drives change across healthcare, finance, retail, and the public sector. They create the models behind product tips, fraud alerts, and data forecasts.

  • Data collection: gathering and cleaning data from many sources for model training
  • Model building: creating and testing ML models to meet business needs
  • Model training: running data through models to boost accuracy and output
  • Model testing: checking model results using set metrics and test methods
  • Teamwork: working with data scientists, software engineers, and business teams to add solutions
  • Research: keeping up with new tools, methods, and findings in machine learning
  • Deployment: releasing trained models into live systems
  • Monitoring: tracking model output over time and updating as data shifts
  • Documentation: recording methods and results for compliance and knowledge sharing

What skills do I need?

Machine Learning Engineers need strong coding skills, especially in Python. Python is the top language in the field. Tools like TensorFlow, PyTorch, and Scikit-learn make model building faster. A solid grasp of data structures, stats, and logic is also key.

Beyond tech skills, these engineers must think clearly and talk to non-technical teams. They often explain model results in plain terms. Working well in a team and handling large datasets is part of the daily job. Keeping up with a fast-moving field is also expected.

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

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About the author

Laura Atkinson

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.