Job Description
This role is for growth engineers who want to own the thing that actually drives revenue.
If you've taken a conversion funnel from 'this probably works' to 'we know exactly why this converts, and we've tested everything', and you want to do that again at a company that's already scaling fast — this is that role.
You'll be owning the full marketing layer: landing pages, programmatic SEO, signup flows, lead magnets, and the experimentation infrastructure that ties it all together.
You run your own roadmap.
The Company
An applied AI company, not SaaS with AI bolted on. They've built a product that eliminates admin overhead for professionals.
Since launching they've sprinted through Series A and Series B raised in under 18 months. Backed by some of the most respected names in the VC landscape and They're on track for >£150M in ARR this year.
Why This Role Is Different
Most growth engineering roles sit downstream of product decisions. This one doesn't.
Growth Engineers here own their domain end-to-end, from hypothesis to experiment to rollout. There are no PMs in the chain. You decide what to test, build it, ship it, and interpret the results.
The company is past the 'does this product work?' stage. The question now is: how fast can we scale acquisition, and how efficiently can we convert? That's your problem to own.
What You'll Own
Conversion Rate Optimisation
* Drive the CRO programme across the full user journey, from paid traffic through to activation and seat expansion
* Design, build, and run multivariate experiments with statistical rigour
* Contribute to experimentation infrastructure: testing frameworks, feature flags, result interpretation, rollout decisions
Marketing Engineering
* Build ICP-targeted landing pages for target customers
* Own programmatic SEO infrastructure and lead magnet micro-apps as conversion channels
* Collaborate with Marketing to align product-side CRO with paid and organic funnels
Data & Growth Strategy
* Work with product analytics, revenue data, session recordings, and customer interviews to identify highest-leverage opportunities
* Identify sign-up quality signals that predict long-term retention and feed them back into acquisition targeting
* Contribute to broader growth loop strategy: virality mechanics, referrals, seat expansion, upgrade flows
The Stack
* TypeScript, React
* Serverless functions, React Query
* Feature flagging tools (GrowthBook, Statsig, PostHog or similar)
* Product analytics and event tracking pipelines
Who Thrives Here
You've shipped experiments that moved the needle and you can point to the numbers. You know the difference between statistical significance and practical significance. You understand why certain copy, layouts, and sequences drive action, and you build around that instinct.