Machine Learning Engineer
Machine learning engineers build the systems that turn data into predictions, then keep those models working reliably once real users depend on them.
- Median salary*
- $127,400
4.2%vs last year, before tax
- People employed
- 94,900
2.4%vs last year
- Projected growth*
- +24%
to 2035
- AI exposure*
- Moderate
- automation risk
- Average hours*
- 39/wk
−1h vs all jobs
- Shortage status*
- In shortage
national
The work sits closer to software engineering than to analysis: a machine learning engineer takes a model that has been shown to work and turns it into a service that runs at scale, usually on a cloud platform. About 94,900 people work in the role in Australia, mostly inside wider engineering or data teams in financial services, technology, government and manufacturing. It is rarely a first job, because employers generally want a foundation in software engineering, statistics or data science before someone takes it on.
How much do machine learning engineers earn?
The median full-time salary for a machine learning engineer is $127,400 per annum, before tax, up $26,800 since 2018.
Pay moves most with how close the role sits to revenue, so a machine learning engineer in a trading, advertising or product team generally earns more than one working on internal tools or in the public sector. Experience with large-scale cloud platforms and a track record of getting prediction systems running in production also lift offers, and contract work carries a premium for the same reason. Seniority separates earnings more than job title does, because the gap between a mid-level engineer and a staff engineer is wide.
What does a machine learning engineer do day to day?
The list below is what fills most weeks; the exact mix shifts with seniority and whatever stage the current work is at.
- Cleaning and joining messy data from several systems long before any model gets near it
- Training and retraining models, adjusting architecture and hyperparameters for a small gain in accuracy
- Writing the pipeline and serving code that puts a model in front of real users
- Watching live models for drift and tracing the cause when predictions start to slip
- Explaining to product managers and analysts why a model that scored well in testing is not ready to ship
What skills do machine learning engineers need?
Employers look for machine learning, data analysis, statistical modelling, backed by Python fluency and strong problem solving.
Specialist skills
- Machine learning
- Data analysis
- Statistical modelling
- Programming and software development
- Cloud infrastructure
- Software testing and QA
Software and tools
- Python
- TensorFlow
- Scikit-learn
- Jupyter Notebook
- AWS SageMaker
General skills
- Problem solving
- Written communication
Is the job growing?
About 94,900 people work as machine learning engineers in Australia, and employment is projected to grow 24% over the decade to 2035. That's very strong growth. Few roles in Australia are expanding this fast, and it points to solid demand for years to come.
How do you become a machine learning engineer?
Here's the path most machine learning engineers take, step by step.
- 1Start with a degree in a computing or quantitative field
Computer science, software engineering, mathematics and statistics are the usual foundations, and around 52% of machine learning engineers hold a bachelor degree as their highest qualification. What matters is the combination: enough programming to build software, and enough maths to understand what is happening inside an algorithm.
- 2Build working skills, not just theory
Python, SQL, version control and at least one framework such as TensorFlow or Scikit-learn cover the basics employers test for. A personal project that goes from raw data to a deployed endpoint says more in an interview than a course certificate on its own.
- 3Get a first role where prediction systems meet production
Most people enter through software engineering, data science or data analytics and move across once they have shipped something real. A software engineer who has deployed a trained algorithm, or a data scientist who has written the pipeline behind one, is the usual candidate.
- 4Consider postgraduate study if you are changing fields
A master's in machine learning or data science is a common route for mathematicians, statisticians and engineers from other disciplines, and around 26% of people in the role hold a postgraduate qualification. It shortens the path into teams that expect research training.
Ready to apply as a machine learning engineer?
Whether you're working toward becoming a machine learning engineer or already are one and want a hand with the next step (sharpening your resume for ATS screening, tightening your cover letter, or knowing what you'll actually be asked at interview), here are examples grounded in this specific role, not generic templates.
What jobs can a machine learning engineer move to?
Moving into Quantitative Analyst typically comes with the biggest pay rise, worth $20,800 a year more on average.
| Move to | Typical pay change | Overlap | Retraining |
|---|---|---|---|
| Quantitative Analyst Machine learning engineers apply statistical modelling and coding to quantitative analysis, moving into finance where those skills are highly valued. | +$20,800 | 50% | short course |
| Quantitative Trader Machine learning engineers with strong maths and programming can move into quantitative trading, applying predictive models to financial markets. | +$20,800 | 50% | short course |
| Software Engineer Machine learning engineers bring strong programming and system design skills to software engineering roles, often moving sideways into broader development work. | +$4,500 | 54% | short course |
| Data Scientist Machine learning engineers bring production and engineering focus to statistical modelling, complementing the analysis work. | −$2,600 | 48% | short course |
| Data Engineer Machine learning engineers understand the infrastructure that feeds models, making them suited to engineering roles that support model deployment. | −$15,600 | 48% | short course |
Moves are chosen from Jobs and Skills Australia's Data on Occupation Mobility, which follows income tax records between 2011-12 and 2020-21, together with entry requirements and skill overlap. A known move is one people were seen making in that data. Pay change compares median full-time pay for the two roles.
Who works as a machine learning engineer?
The typical machine learning engineer is 37 years old; 84% are men, 90% work full-time, and full-timers average 39 hours a week.
- 37
- Median age
- 16%
- Female share
- 90%
- Full-time
- −1h
- vs all-jobs avg
What's it like being a machine learning engineer?
Most of the week goes to data preparation, pipeline code and testing, with the modelling itself taking a smaller share than people picture. The rhythm runs from an experiment that looks promising to the longer stretch of engineering that makes it dependable, and much of the pressure comes from systems that behave differently once live data arrives. It suits people who are comfortable in both maths and code, and who do not mind that the interesting problem and the useful result can be weeks apart.
What people like
- Seeing the work run in production. A result in a notebook is an experiment; the same output serving live traffic and holding up under load is the part engineers describe as satisfying.
- Problems with a measurable payoff. Fraud detection, recommendations and forecasting all come with numbers attached, so it is usually clear whether the work helped or not.
- Room to specialise. Natural language, computer vision, recommendation systems and the operational side of deployment each have their own depth, and the field rewards going further into one of them.
- Ideas that move quickly. New techniques appear in papers and land in open-source libraries within months, which suits people who like testing an approach rather than following a fixed method.
What people find hard
- Weeks of data work before the modelling starts. Cleaning, labelling and joining data often takes longer than the training itself, and none of it looks like the job people picture.
- Results that do not hold up. An approach that scored well in testing can behave differently against live data, and finding out why usually means going back through the data.
- Explaining uncertainty to people who want a number. Product managers and clients often want a yes or a no, and a probability takes longer to explain and defend.
- Keeping up is part of the job. Frameworks, cloud services and published methods change quickly enough that reading and experimenting sit inside the working week.
Based on our synthesis of professional-body surveys and public accounts of the role, not first-person verified reviews.
Which industries employ machine learning engineers?
Professional, Scientific and Technical Services employs the largest share of machine learning engineers, followed by Financial and Insurance Services.
Top employing industries
- 1Professional, Scientific and Technical Services
- 2Financial and Insurance Services
- 3Information Media and Telecommunications
- 4Public Administration and Safety
- 5Manufacturing
Ranked by employment share; the source doesn't publish an exact percentage per industry.
| Bachelor degree | 52% | |
|---|---|---|
| Postgraduate | 26% | |
| Diploma / Advanced Diploma | 13% | |
| Other | 9% |
Will AI replace machine learning engineers?
Machine learning engineering sits in the middle: the tools of the trade automate parts of the job and shape the rest of it. AutoML services and hyperparameter search now run many of the training experiments that were once hand-tuned, and platforms such as SageMaker handle a good deal of the plumbing. The parts that stay with the engineer are framing the problem, getting data into a usable state and deciding whether the result is good enough to put in front of users.
Share of typical working time by exposure level
- Building the pipelines that serve predictionsDeployment, monitoring and rollback code is ordinary software engineering, and an assistant speeds up the typing without designing the system or choosing the trade-offs.30%low
- Cleaning and preparing dataProfiling tools flag missing values and outliers quickly, but fixing them safely still means knowing where the data came from and what the column is supposed to mean.25%moderate
- Training and tuning algorithmsAutoML and hyperparameter search services run hundreds of trials overnight, so the engineer sets the search space and the metric rather than the loop itself.25%high
- Monitoring live systems and diagnosing failuresDrift dashboards raise the alarm, but working out whether a drop in accuracy comes from a change in user behaviour or a broken upstream feed is still manual.20%moderate
Common questions about becoming a machine learning engineer
Straight answers to the questions people ask most.
How much do machine learning engineers earn in Australia?
Machine learning engineers earn $127,400 per annum, before tax, as a median full-time figure. Sector and specialisation move it most, along with whether the role is product-facing or research-focused.
How do you become a machine learning engineer?
The usual route is a degree in computer science, software engineering or a quantitative field, then a first job in software engineering or data work that involves deploying trained algorithms. Machine learning engineering is rarely the entry point itself, because employers look for evidence you have taken something to production.
Are machine learning engineers in demand in Australia?
Machine learning engineers are currently in shortage nationally, and employment is projected to grow 24% over the decade to 2035. Openings cluster around technology, financial services and government, and most expect prior experience, so a first role in software or data is the practical way in.
Will AI replace machine learning engineers?
AI already does part of this job: automated search tools handle much of the tuning that used to take weeks, and coding assistants write plumbing code quickly. What stays with the engineer is framing the problem, getting the data into a usable state and deciding whether the result is good enough to ship. The role is more likely to shift towards those parts than to disappear.
What can machine learning engineers move into?
Software engineering is the closest sideways move, where the programming and system design carry over and pay is $4,500 more. Quantitative analyst and quantitative trader roles attract people with strong maths, applying statistical prediction to financial markets. Data science and data engineering are the other common moves, depending on whether you prefer analysis or the infrastructure that feeds it.
Can people from other backgrounds move into machine learning engineering?
Statisticians and mathematicians are the common entrants, since their statistical foundations cover much of the groundwork and they need little retraining to pick up the engineering side. Data scientists move across as well, usually to work on productionising and scaling the systems they already build.
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