Machine Learning Engineers – Skill Gaps in Australia

Global Employability
July 30, 2025


The Australian Public Service is implementing the 2025–30 Digital, Data & Cyber Workforce Plan, highlighting shortages in data, digital, and cyber roles and promoting upskilling, career pathways, and recruitment reform. No matter which field we work in, technology seems to be at the forefront.

At present, artificial intelligence (AI) and machine learning is at the core of most digital technologies today across a variety of fields and applications. As such, the demand for those with relevant skills in the areas is welcome in most parts of the world – including Australia! But the number of engineers that are available on hand is too low compared to the demand. Why? Only ~1% of tech graduates are considered immediately work-ready, and governments are promoting ‘earn while you learn’ models and micro‑credentials to close the gap



Digital Australia - Or Is It?


In the past couple of years, we have seen the tide turn in favour of more robust remote digital technologies that have been enabling people all over the world to work from their homes. Scientific development in various fields, especially in extended reality, remote technologies, cloud computing, machine learning, and autonomous vehicles have increased manifold and most of these trends are sure to stay on in a post-COVID world.

Industry leaders and policymakers are calling for increased tech migration—650,000 new tech workers by 2030 is in focus—and urging urgent investment in AI infrastructure, data centres, and telecom upgrades. In fact, the Australian Academy of Science and the Australian Academy of Technology’s new joint report addresses the digital gap that Australia faces and the necessary steps it needs to take in order to embrace the digital future. Alex Haloulos, director of Sage Software opines that Australian organisations are being held back by the “lack of highly skilled workers”.

Recent industry analysis suggests digital job vacancies are ~60% higher than the national average, and digital roles are expected to grow three times faster than average through 2025. Australia needs approximately 300,000 additional tech workers by 2030, with 52,000 tech professionals needed each year through 2030 to support AI infrastructure and digital growth. Emerging fields now include quantum computing, 5G/6G networking, smart-energy microgrids, and industry-recognized certifications like Microsoft’s AI-900, which significantly boost job-readiness.

In Australia, the top in-demand skills include cloud computing, cybersecurity, data engineering & science, AI and machine learning, automation (RPA), and cloud architecture, with advanced cloud skills among the top 5 digital priorities for employers by 2025.

Machine Learning Skill Gaps in Australia


  • STEM background, Statistical and Analytical Skills – In order to build efficient machine learning algorithms, it is important to understand how they work from a mathematical standpoint. In fact, most algorithms are designed using mathematical and statistical formulae and methods. Analytical skills are critical to understand what a collection of data means or describes. Having a background in science, technology, engineering or mathematics is critical to becoming an ML engineer.
  • Programming Skills – To become a good ML engineer, one needs to understand how to program first. By learning how to program in different languages including Python, R, C++, C, Java, Scala, and Go, one can efficiently implement ML models to address various real-world problems. One must also be familiar with environments such as Matlab, Hadoop, and Spark.
  • Data science skills – ML requires data in large amounts and this data requires a lot of modelling or pre-processing before it can be used for analytical or training purposes. Therefore, understanding how to handle large amounts of data (data modelling) is an important skill.
  • Problem Solving and Critical Thinking – ML methods require a fair amount of critical thinking in order to figure out what ML model is to be applied to the kind of problem at hand. Since every data set that passes through these models is unique and since every output varies based on how we set the parameters, it is pretty important to possess critical thinking capabilities.
  • Understanding ML-related technologies – This includes understanding ML workflows and models, using simulation tools, mastering frameworks such as TensorFlow, exploring NLP packages, and working with robotic process automation.
  • Communication Skills – Great communication skills is the hallmark of a good ML engineer because one will be working with a lot of non-technical individuals. Also, it isn’t always about what data conveys but how you put it to good use.

Ready to Upgrade Your Tech Career?


Want to stand out in Australia’s booming digital landscape? Whether you’re striving to become an AI specialist, machine learning engineer, or cloud expert, you don’t have to go it alone.
Let The Chopras’ expert counsellors help you map your pathway—whether it’s choosing the right course, writing a standout SOP, or building your tech profile.

Book Your Free Consultation Today

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Step into Australia’s digital future with confidence.



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