AI engineer salary benchmarks Melbourne 2026
What ML, MLOps, GenAI, and Data engineers are actually getting paid in Melbourne right now. Permanent base salaries, contract day rates, and what drives the spread.
Updated April 2026. Numbers below come from active placements and offer-stage data we've seen across our network in the last 90 days. Treat them as a directional band, not gospel — the spread is wide and seniority interpretation varies between companies.
Permanent base salaries
| Role | Junior (1-3 yrs) | Mid (3-6 yrs) | Senior (6+ yrs) | Staff / Lead |
|---|---|---|---|---|
| ML Engineer | $130-160K | $160-200K | $200-260K | $260-320K+ |
| GenAI / AI Engineer | $140-170K | $170-220K | $220-280K | $280-350K+ |
| MLOps Engineer | $140-170K | $170-220K | $220-270K | $270-330K+ |
| Data Engineer | $120-150K | $150-190K | $190-240K | $240-290K+ |
| Applied Research Scientist | $140-180K | $180-230K | $230-300K | $300-380K+ |
Numbers are AUD, base only, excluding super and equity. Add 10-15% for cash-rich US-headquartered employers; subtract 5-10% for non-tech industries (healthcare, government, traditional finance).
Contract day rates
| Role | Junior | Mid | Senior | Lead |
|---|---|---|---|---|
| ML Engineer | $700-900 | $900-1,100 | $1,100-1,400 | $1,400-1,700 |
| GenAI Engineer | $800-1,000 | $1,000-1,300 | $1,300-1,600 | $1,600-2,000 |
| MLOps Engineer | $700-900 | $900-1,200 | $1,200-1,500 | $1,500-1,800 |
| Data Engineer | $650-850 | $850-1,050 | $1,050-1,300 | $1,300-1,600 |
GenAI commands a premium because production experience is genuinely scarce — most candidates have built demos, few have shipped agents that take real user money.
What drives the spread
Production experience is the #1 differentiator. A candidate who's deployed an LLM-backed feature handling 100K+ daily requests is in a different band from someone who's done the same work as a side project.
Cloud-native MLOps experience adds 10-20%. Engineers who can stand up an ML platform on AWS/GCP from scratch — feature store, registry, CI/CD, monitoring — are scarcer than people realise.
On-call / production ownership. Engineers willing to own the pager for ML systems get paid for it. Companies that won't pay this premium end up with research-only hires and DevOps engineers cleaning up the mess.
Specialisation pays. Computer vision specialists in retail / manufacturing, NLP specialists in legal / healthcare, RecSys specialists at consumer-scale all command 10-15% over generalist ML.
What's softening
Generalist data scientists with notebook-only experience are getting harder to place at senior bands. The market has matured — companies want production-grade engineering, not just analytical depth.
What's heating up
Anything involving agents — the gap between "I built a chatbot" and "I built a multi-step agent in production with eval, guardrails, tool use, and on-call" is vast, and the second band is being bid up sharply.
Methodology
These bands come from offer-stage data on placements completed since January 2026 plus rejected counter-offers we've seen at companies in our network. We don't publish percentile breakdowns because the sample at any given seniority is too small to be statistically robust — treat the bands as a triangulation, not a survey.
If you want a tighter band for a specific role, book a 15-minute call. We'll walk you through what the last three placements at that level cost.