AI, Data,
& Machine Learning
Production-grade ML talent - not paper-only researchers.
We hire for firms shipping ML, not publishing it. The strongest candidates have a track record of models in production, not just citations on arXiv.
What we do here
This is one of the harder talent markets in the country. Strong applied ML engineers in Australia number in the low hundreds. We map proactively rather than rely on inbound channels, because the engineers worth hiring are rarely actively looking.
What “great” looks like at intake
We need to understand the production-vs-research split clearly. We will also push on compensation: this market is paying offshore-equivalent rates for the top quartile, and underbidding the band is the most common cause of failed searches we see.
Recent engagements in this sector
A staff-level CV search for a defence-technology client; multiple senior ML engineers placed into a vertical-SaaS firm building proprietary models; a Head of Data appointment into a regulated fintech.
Capabilities we cover
- ▸ Computer vision and detection systems
- ▸ NLP and large language models
- ▸ Data engineering and analytics infrastructure
- ▸ Applied research and ML platform
Typical roles & bands
- Senior / Staff ML Engineer$150k-$220k base
- Computer Vision Lead$180k-$260k base
- Head of Data / ML$220k-$320k total
- Data Engineer (senior)$140k-$190k base
- Source. The founder's placement records across 15 years of recruitment practice, continuing into AEY, plus ongoing market benchmarking.
- Composition. Base salary unless otherwise specified. Total-comp roles include equity / bonus / day-rate where stated.
- Variance. Actual offers move with stage, location, technical specialism, clearance status and the candidate's alternative options. The bands above are 25th–75th percentile, not floor and ceiling.
- Currency. All amounts in AUD.
Market notes
The hiring market has split: pure research talent (ex-FAANG, ex-academic labs) versus applied engineers with a deployment track record. The right answer almost always depends on whether the work is research-led or product-led. We will tell you, on the first call, which one your brief actually is.
Sydney + Melbourne concentrated; split between research-led and product-led talent.
Anonymised, never shared. Maintained as a live working map, not a database.
Let's talk about ai, data & machine learning.
A 30-minute call is enough for us to know whether we can add value - and whether you should be talking to us, or to someone else.