How to Hire an ML Engineer in Australia

Oct 9

3 min read

Updated: Oct 15

Machine learning engineers are among the hardest tech roles to fill in Australia. Demand has grown with AI adoption, and the local pool of people with production ML experience is small. This guide covers what the role is, what to look for and how to find the right person.

What an ML engineer does

An ML engineer takes models from experiment to production. They build the pipelines that train, test, deploy and monitor models. They make sure a model works on real data, at real scale, and keeps working after launch.

A data scientist asks which model answers the business question. An ML engineer makes that model run reliably inside your product. Many companies need both, and many hire the wrong one first.

Decide what you need before you hire

ML engineer is a broad title. Get clear on these points before you write the job ad:

Problem type: forecasting, recommendations, computer vision, language models or something else.
Stage: are you proving an idea, or running models in production for customers?
Team: will this person work alone, or join an existing data or platform team?
Stack: your cloud platform, data tools and deployment setup.
Scope: classic ML, LLM and agent work, or both.

A first ML hire at a scaleup needs breadth and independence. A hire into a large ML team needs depth in one area. The same title covers very different people.

Skills to look for

Strong Python and software engineering habits: testing, code review, version control.
Experience shipping models to production, not only notebooks.
MLOps: model serving, monitoring, retraining and experiment tracking.
Cloud experience on AWS, Google Cloud or Azure.
Data engineering basics: SQL, pipelines and data quality checks.
For AI products: experience with LLMs, retrieval, evaluation and cost control.

The best signal is a model they built that real users relied on. Ask what broke after launch and how they fixed it.

Where to find ML engineers in Australia

Most strong ML engineers are employed and not looking. Job ads reach a small part of the market. Look in these places:

Software engineers at product companies who moved into ML work.
Data scientists who own deployment and production code.
Engineers at consultancies who build ML systems for clients.
Researchers from universities and labs who want applied work.
Australians working overseas who want to come home.

Direct approaches work best. Explain the problem, the data and the impact of the role. ML engineers care about interesting problems and good data more than perks.

How to run the interview

A technical screen on Python and ML fundamentals.
A system design session: design a pipeline for a problem close to yours.
A deep dive on a past project, from data to production to monitoring.
A conversation with the team they would join.

Keep the process short. Strong ML engineers often hold more than one offer. A process longer than three or four weeks loses candidates.

Related roles

Some AI products need an engineer who works with customers to deploy models in their systems. Read our guide on forward deployed engineers.

How Staff Hunter helps

Staff Hunter recruits ML engineers, AI engineers and senior data people for startups, scaleups and established businesses in Australia. We find people through direct headhunting. See our data and AI/ML recruitment page, or book a 30-minute call to talk through your role.

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