GTM Engineer

Lorikeet

About The Position

We'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.

Requirements

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
Strong communication skills: you'll work across sales, product, and marketing

Nice To Haves

Consulting or product background is a plus — as is experience in growth or as an SDR/BDR

Responsibilities

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