Machine Learning Engineer Salary in Australia 2026

Machine Learning Engineer is still a young title in the Australian job market. Unlike roles such as software engineer or business analyst, which have decades of salary survey history behind them, ML engineering only started appearing as a distinct, high-volume hiring category in the last few years, as generative AI adoption pushed employers to build dedicated model and MLOps teams rather than folding the work into broader data science functions. Local AI adoption has also moved more cautiously than in the US, which means the sample sizes recruiters and salary platforms are working with are smaller and the numbers move around more than they do for established roles.

This guide pulls together salary data from the platforms and recruiters currently tracking the role in Australia, so you can see where the market sits.

What does a Machine Learning Engineer do?

A Machine Learning Engineer builds, trains, deploys, and maintains the models that power predictive features, recommendation systems, and increasingly, generative AI applications. The role sits at the intersection of software engineering and data science: ML engineers write production-grade code, build data pipelines, manage model training and evaluation, and take responsibility for getting models running reliably in live systems.

Day to day, that can mean feature engineering and data preprocessing, training and fine-tuning models, building MLOps infrastructure for deployment and monitoring, and working closely with data scientists, data engineers, and product teams to translate business problems into working systems. As generative AI has matured, many ML engineers have also picked up work fine-tuning and evaluating large language models, though that overlaps increasingly with the newer "AI Engineer" title (more on that distinction below).

It's a role that suits people who are comfortable moving between research-style experimentation and disciplined software engineering, since a model that performs well in testing is only useful once it's been engineered to run reliably in production.

How much does a Machine Learning Engineer earn in Australia?

*data is as of July 2026

Because the role is new and the sample sizes are small, different platforms are pulling from different pools of respondents and job ads, and the spread between sources is wider than you'd see for a more established role like software engineer or business analyst.

Even the "average" figures disagree by close to $60,000 at the midpoint. Glassdoor and Indeed, which lean on self-reported and aggregated job-ad data, sit noticeably lower than the recruiter-reported numbers from Clicks, Robert Half, and Hays. That pattern will look familiar from other tech salary guides where self-reported platforms tend to skew toward junior listings, older submissions, and generic "data/ML" job titles, while recruiter guides are built from what companies are actually agreeing to pay to close active roles, which tends to be weighted toward mid-to-senior hires.

Taken together, a reasonable working range for a Machine Learning Engineer in Australia in 2026 is roughly $130,000 to $200,000+ in base salary, with the top end climbing well past $250,000 for senior and lead-level hires at large tech employers or well-funded scale-ups.

Part of the reason the spread is so wide is structural. Machine learning talent in Australia has been in acute shortage: Bain and Company's research estimated the number of AI specialists in the local workforce would need to more than double from around 40,000 in 2024 to 85,000 by 2027, and even then the country would still face a shortfall of up to 60,000 AI professionals against forecast demand. PwC Australia's 2026 AI Jobs Barometer found that AI-skilled workers now command an average wage premium of 62 percent over non-AI roles, up 57 percent from the year before which is a sharp premium that's still being priced inconsistently across the market as employers work out what these skills are actually worth to them.

Machine Learning Engineer salary by experience level

Here's where we need to be honest about the limits of the data: because ML engineering is such a recently formalised title in Australia, none of the major salary sources currently publish a reliable, ML-specific breakdown by years of experience. Cyber security, software engineering, and other more established roles have years of survey history behind their junior/mid/senior bands. ML engineering doesn't yet, largely because the role has only existed at meaningful hiring volume for a few years, and because titles and seniority bands still vary a lot between companies — a "senior" ML engineer at one startup can be doing work that would carry a "lead" title somewhere else.

What we can say with more confidence, drawing on the broader ranges above and general placement patterns, is directional:

• Engineers in their first two to three years, often moving across from data science, data engineering, or software engineering, tend to sit toward the lower-to-middle end of the ranges above.
• Engineers with three to six years of applied ML experience, particularly with production deployment and MLOps skills, cluster around the middle of the market — roughly around the Robert Half 50th percentile and Clicks/Glassdoor upper bands.
• Senior and lead-level engineers, especially those with generative AI and LLM fine-tuning experience, are the ones pushing salaries toward the Hays $250,000 ceiling and beyond.

Treat this as a general shape rather than a firm ladder. If you're benchmarking an offer or negotiating one, it's worth asking a specialist recruiter what similar-seniority ML roles have actually closed for recently, since that will tell you more than any published band.

Machine Learning Engineer salary by city

City-level data is available from PayScale (self-reported), Hays, and Robert Half, and the pattern here is consistent with what we see nationally: self-reported figures sit meaningfully below what recruiters report closing roles at.

Averaging the recruiter-reported midpoints (Hays and Robert Half) against PayScale's self-reported midpoint, the recruitment data runs roughly 78% to 90% higher than what candidates self-report on PayScale. Depending on the city, this number is closer to 80% in Sydney and Melbourne, and closer to 90% in Brisbane. That's a bigger gap than you'd typically see in more mature roles like cyber security analyst or software engineer, where recruiter and self-reported figures are usually within 20–40% of each other.

A few things likely explain the size of the gap. Self-reported platforms like PayScale draw from whoever chooses to submit a salary, which skews toward junior professionals, broader "data/ML" titles, and older submissions in a fast-moving field. Recruiter guides, by contrast, are built from live placement data for roles actively being hired right now, which naturally skews toward the mid-to-senior, high-demand end of the market where employers are willing to pay a premium to secure scarce talent.

Contractor vs permanent

Contract data for ML engineers is thinner than for permanent roles, but two sources give us a starting point.

Annualised roughly (250 working days), Clicks' figures put contract ML engineers at approximately $233,750 to $285,000 gross per year at the entry-to-senior range, well above the equivalent permanent bands. As with other roles within the tech industry, that premium needs to be weighed against what permanent employment includes: 11.5% superannuation, four weeks of paid annual leave, sick leave, and often a training or compute budget that funds courses, certifications, or cloud credits.

Given how new and specialised the skill set is, contracting tends to suit ML engineers who already have a strong track record of shipping models to production and don't need the ramp-up time or mentorship that comes with joining a permanent team. Given how thin the day-rate data still is compared to more established roles, it's worth treating these figures as a starting benchmark rather than a firm ceiling, and checking current rates with a specialist recruiter before settling on a number.

Machine Learning Engineer vs AI Engineer

If you're mapping out a career path, or trying to write an accurate job ad, it's worth understanding how "Machine Learning Engineer" differs from the newer "AI Engineer" title, since the two are often used interchangeably despite meaningfully different day-to-day work.

The general consensus across recent industry writing is that a Machine Learning Engineer builds models largely from scratch or from established ML libraries. Things like gradient-boosted trees, computer vision models, or custom neural networks, owning the full pipeline from data preprocessing through to deployment and monitoring. An AI Engineer, by contrast, typically specialises in integrating and orchestrating existing foundation models. Think large language models like Claude or GPT, using them to turn into products, with less emphasis on training models from the ground up and more on system design, prompt engineering, and application-layer integration.

In practice, the lines blur constantly, and job titles in Australia are applied inconsistently between employers, with some companies using "AI Engineer" for what is functionally a traditional ML role, largely because it reads as more current in a job ad. This overlap is part of why salary data for both roles is noisy: platforms and recruiters aren't always segmenting consistently, so an "AI Engineer" salary figure in one dataset may cover work that another dataset would classify as ML Engineering, and vice versa.

The job market for Machine Learning Engineers in 2026

LinkedIn's Jobs on the Rise 2026 report named AI Engineer the fastest-growing role in Australia, up around 150%, and the Technology Council of Australia projects the national AI specialist workforce needs to more than double by 2027 just to keep pace with demand. Indeed's Hiring Lab data shows the share of Australian job ads mentioning AI has roughly doubled year-on-year, and industry commentary consistently flags machine learning engineers among the roles facing the most severe shortages, even as demand for more traditional software development roles has started to ease.

The honest reality is that Australia's AI adoption curve has been slower and more cautious than the US or parts of Asia, and that's part of why salary benchmarking for ML-specific roles remains harder than for established tech roles. As adoption deepens and more companies build dedicated ML and AI functions rather than folding the work into general data teams, expect the salary data to become more granular and more consistent — but for now, treat any single figure as a data point to sanity-check against current market conversations, not a fixed benchmark.

Frequently asked questions

What is the average Machine Learning Engineer salary in Australia?

Roughly $130,000 to $200,000+ depending on the source, with self-reported platforms like Glassdoor and Indeed sitting lower (around $117,000–$158,000) and recruiter-reported guides from Clicks, Robert Half, and Hays running higher (up to $250,000 at the top end). The spread between sources is wider than for more established tech roles, reflecting how new and fast-moving the role still is in Australia.

Why is there so little salary data for Machine Learning Engineers by experience level?

Because the role has only existed as a distinct, high-volume hiring category in Australia for a few years, salary platforms haven't yet built up the years of submissions needed for a reliable junior/mid/senior breakdown the way they have for roles like software engineer or cyber security analyst. Titles and seniority bands also still vary significantly between companies.

Is Machine Learning Engineer a good career in Australia?

Demand is strong and growing — AI Engineer was named the fastest-growing role on LinkedIn's 2026 Jobs on the Rise list, and multiple industry reports point to a persistent national shortfall in AI and ML talent through at least 2027. Pay sits well above the national average, though the market is still maturing, which means less predictability than in more established tech roles.

What's the difference between a Machine Learning Engineer and an AI Engineer?

Machine Learning Engineers generally build and train models from data, owning the pipeline from preprocessing through deployment. AI Engineers typically focus on integrating existing foundation models (like large language models) into products and systems. In practice, Australian employers use the two titles inconsistently, which adds noise to salary comparisons between them.

Is it better to contract or take a permanent Machine Learning Engineer role?

Available day-rate data (Indeed's $725/day average and Clicks' $935–$1,140/day range) suggests contracting can pay significantly more in gross terms, but permanent roles include superannuation, leave entitlements, and often training or compute budgets. Given how thin the contracting data still is for this role, it's worth treating published rates as a starting point and confirming current numbers with a specialist recruiter.

Need help hiring a Machine Learning Engineer?

If you're building a security team or looking to fill a ML Engineer role, we can help. Latitude IT places ML Engineer professionals across Sydney, Melbourne, Brisbane, and Perth, and we benchmark every role against current market data from industry sources to make sure your offer is competitive from day one.

Talk to George Bates→ George supports our AI, Product & Engineering recruitment practice and places them every week. If you want to know what the market looks like for your specific requirements, he's the person to talk to.

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Sources: Clicks IT Recruitment, Glassdoor, Indeed, Robert Half 2026 Salary Guide, Hays Salary Guide, PayScale, Bain & Company (via InnovationAus), PwC Australia 2026 AI Jobs Barometer, Big Wave Digital State of AI Hiring in Australia 2026, Towards Data Science.