Lorikeet is redefining what customer support looks like — and we're just getting started.
We build AI that handles complex, high-volume support for fintechs, healthtechs, marketplaces, and delivery services. Not chatbot-tier AI. The kind that actually resolves tickets, gives support teams their time back, and raises the bar for what software can do.
We're backed by over $50M USD from QED, Blackbird, and Square Peg, with backing from operators like Claire Hughes Johnson (ex-Stripe COO) and Cristina Cordova (Linear COO), plus founders of Canva, Atlassian, and Airwallex. We're the first company since Canva to be funded at early stage by all three of Australia's top VC funds — which tells you something about the calibre of people who've looked closely at what we're building and said yes.
Our customers include some of the largest telehealth, fintech, and marketplace companies in the US and Australia — many handling over a million support tickets a year. The problems are real, the scale is real, and the work matters.
If you want to build something that genuinely changes how businesses and customers interact — this is the place.
About the roleWe're looking for a GTM Engineer to own the systems that generate pipeline — someone who thinks like an engineer but executes like a marketer. You'll build AI-powered marketing infrastructure, run signal-based prospecting at scale, and drive growth loops across organic, paid, and lifecycle marketing.
This isn't a traditional demand gen role. You'll use Claude Code, Clay, and modern AI tooling to build things that would have required a team of five a few years ago. You'll own experimentation velocity, not just campaign execution.
You'll report to the Head of Marketing and work cross-functionally with sales, product, and forward-deployed engineering.
Build AI-powered marketing infrastructure
Design and operate AI agents that run marketing workflows autonomously — including lead enrichment, campaign personalisation, content production, and signal monitoring
Build experimentation infrastructure that tests messaging, channels, and sequences faster than traditional marketing teams thought possible
Create self-improving systems with evals, feedback loops, and guardrails that get better without constant supervision
Run signal-based prospecting
Build and optimise outbound engines using Clay, Apollo, RB2B, Unify, Instantly, and similar tools
Surface buying signals (hiring, funding, competitor moves, contract renewals) and route them to sales with full context
Design enrichment workflows that prioritise accounts automatically and identify warm intro paths
Drive growth loops
Own lifecycle marketing: nurture sequences, re-engagement campaigns, and credit consumption alerts that drive expansion
Run 1:many ABM campaigns targeting industry marketing account lists
Build and iterate on paid campaigns (LinkedIn, Google, Reddit) with rapid A/B testing
Connect organic content (Steve's LinkedIn, SEO/GEO) to conversion infrastructure
Own experimentation velocity
Run 4–6 marketing experiments per month with clear hypotheses, metrics, and iteration cycles
Document what works and systematise successful experiments into repeatable playbooks
Track pipeline influence, reply rates, meetings booked, and cost per meeting across all channels
3–6 years in growth marketing, marketing ops, revenue ops, or a hybrid sales/marketing role at a B2B SaaS company
Genuine fluency with AI tools — building with Claude Code, Cursor, or similar, not waiting for someone else to build for you
Experience with the modern GTM stack: Clay, Apollo, HubSpot, Unify, or similar (or demonstrated ability to learn fast)
Quantitative mindset: you think in funnels, conversion rates, cohort analysis, and attribution models
Comfort with ambiguity: we're building the playbook, not executing an existing one
Consulting or product background is a plus — as is experience in growth or as an SDR/BDR
Strong communication skills: you'll work across sales, product, and marketing
