How to get a job at an AI startup in Australia – In 2026, Australian AI startups are hiring selectively for impact-ready roles: applied ML/LLM engineers, full-stack product builders, and people who can evidence safe, privacy-aware delivery. Use this guide to map the hiring channels, build proof fast, and approach founders with credibility.
Where Australian AI startups are hiring and what they value
Hiring is clustered around Sydney, Melbourne, and remote-friendly teams that work on AEST hours. Early-stage founders prioritise broad builders who can ship end-to-end (frontend, backend, model integration, evals). Later-stage teams add specialists: ML infra, data governance, and applied research. Expect lean salary bands with equity; junior roles often ask for demonstrable output over years of experience.
Download the How to get a job at an AI startup in Australia checklist
Access a structured template to apply the steps in this guide.
💡Show momentum, not just interest
Add links to live demos, short Loom videos, and a one-page README on risks/mitigations. Founders skim; make it easy to see that you build, ship, and think about safety.
Building a portfolio Australian founders will actually open
Prioritise applied work: a small product that uses an API model, a custom prompt router, or lightweight retrieval with evals. Keep repos lean with setup instructions, a short video, and a note on how you handled personal data under the Australian Privacy Act. Two strong projects beat eight half-finished notebooks.
Proof points that land interviews
Include: (1) before/after metrics or user feedback, (2) how you evaluated prompts or models, (3) safeguards (rate limits, PII filtering), and (4) decisions you made on hosting costs. If you lack production access, use synthetic data and explain your assumptions.
Practical steps
- 1Ship one end-to-end demo with a short Loom walkthrough
- 2Document evals and a privacy note in your README
- 3Publish a concise LinkedIn post tagging the stack you used
Expert insight
“Founders skim for signal: a link that works, a README that shows judgment, and evidence you can deliver in a week—not a promise you’ll learn it later.”
Finding live roles and hidden opportunities in Australia
Combine public boards with founder-led channels. Check Seek (filter “AI/ML”), LinkedIn, Wellfound, and YC’s Work at a Startup. Watch university lab newsletters (e.g., UNSW/UTS/UniMelb AI groups), and follow Australian AI founders on LinkedIn. Hidden roles surface in Slack/Discord communities and at meetups—turn up, ask what’s shipping, and offer a small contribution.
💡Fast signal outreach
Send a 5–7 line note with one relevant link: “I built this prompt-eval harness last week; here’s a Loom. Keen to contribute to your safety backlog—can we pair for 30 minutes?”
Application strategy: CV, cover note, and timing
Keep CVs to one page with a “Shipped AI work” section up top. Lead with outcomes (e.g., “Reduced manual review time by 30% via RAG prototype”). Use a short cover note tailored to the product, mention availability in AEST, and flag work rights. Apply early in the week; many teams triage on Mondays.
Practical steps
- 1Create a one-page CV with a top “Shipped AI work” section
- 2Draft a 7-line cover note tailored to the product and market
- 3Apply Sunday night or Monday morning; set reminders to follow up in 5 days
Interview formats, take-homes, and safety expectations
Expect: a founder or hiring manager screen (15–20 minutes), a practical take-home (prompt design, lightweight RAG, or UI wiring), and a pairing session. Some teams include a privacy or red-teaming scenario. Be ready to explain trade-offs: cost vs latency, evals vs release speed, and how you’d handle PII for Australian users.
Prepare concise examples
Bring two stories: (1) when you added safeguards after a failure, and (2) when you shipped a scrappy experiment in days. Keep answers structured (situation, action, result, what you’d change).
🛡️Safety & compliance checkpoint
Mention how you would minimise personal data, log access, and align with the Australian Privacy Act. If unsure, propose a lightweight data handling checklist and a post-release eval pass.
Work rights, remote-first norms, and pay expectations
Most early-stage teams need candidates with existing Australian work rights. Remote roles often require AEST overlap. Pay bands vary: early-stage may offer lower cash with equity; later-stage scaleups pay closer to market for engineers and product builders, with premiums for ML infra and security talent. Always confirm superannuation and equity terms in writing.
60-day plan to reach your first AI startup interviews
Sequence your effort: build proof, show up where founders are, and send targeted outreach with one strong link. Track applications in a simple spreadsheet and adjust weekly based on replies.
Your Next Steps
- 1Download the checklist mentioned above.
- 2Draft your initial goals based on the template.
- 3Discuss with your team or mentor.
Free MLAI Template Resource
Download our comprehensive template and checklist to structure your approach systematically. Created by the MLAI community for Australian startups and teams.
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