Role

Hire your AI Growth Engineer

Ship a new experiment every week. Track everything. Keep what works.

Your AI growth engineer is the hands that build, instrument, and analyze experiments. It builds landing pages, wires up event tracking, runs split tests, and reports deltas in plain English. You get the scientific method without having to become a full-stack engineer, PostHog expert, and CRO consultant simultaneously.

Free to startNo credit card requiredUpdated Apr 2026

What your AI Growth Engineer does

01Build and ship landing pages, lead magnets, and conversion surfaces within a day each
02Instrument analytics end-to-end: events, funnels, attribution, cohorts, revenue tracking
03Design and launch A/B tests with pre-registered hypotheses and measurement windows
04Write the statistical readout: effect size, confidence interval, practical significance
05Maintain a running experiment log the AI CEO reads into weekly strategy
06Debug tracking gaps — 'why is Stripe not mapping to user_id in PostHog?' — in hours, not weeks
07Stand up lightweight tools (onboarding quizzes, referral widgets) without waiting for a roadmap slot
08Report weekly on which experiments hit, missed, or are inconclusive — with next-step recommendations

Workflows on autopilot

Weekly experiment cycle
Reads the backlog Monday, picks the highest-expected-lift test, ships the build by Wednesday, instruments tracking Thursday, launches Friday, measures through the following week.
Landing page drop
Takes a new campaign brief from the AI CMO, ships a focused landing page in 24 hours, wires conversion events, and queues it for paid traffic.
Tracking audit
Monthly sweep: verifies every critical event fires, checks for duplicate tracking, confirms attribution chains aren't broken, rewrites flaky event names.
Funnel diagnostics
When a core metric drops, queries PostHog and Stripe to isolate which funnel step regressed, and reports the root cause in a single chart.
Experiment readout
Every Friday produces a 1-page readout: what shipped this week, what we learned, what's next. Sent to the AI CEO and founder in chat.

Without vs With a AI Growth Engineer

Without
  • Experiments get designed but never shipped because landing pages are a two-week backlog item
  • Analytics are 'mostly working' and you don't trust the funnel numbers
  • You run 1 test a quarter and each one is inconclusive
  • You hire a $200K growth engineer and wait 3 months for them to understand the stack
  • 'We should test that' dies in Slack
With Tycoon
  • New landing page every week, ready for paid or organic push
  • Monthly audit keeps tracking tight; you actually make decisions from the data
  • One test a week with pre-registered hypothesis and proper stats
  • AI growth engineer reads your stack in a day and ships the first experiment in the first week
  • Ideas hit the Linear backlog, get prioritized, and ship on cadence

A day in the life of your AI Growth Engineer

07:15
Pulls overnight experiment data from PostHog. Checks for significance on the pricing-page test that launched last Friday.
09:00
Ships a new lead magnet landing page — form, Stripe checkout, event wiring. 3 hours from brief to live.
12:30
Debugs why Stripe subscription_created events stopped mapping to user_id in PostHog. Finds a missing identify() call, ships the fix.
14:30
Designs next week's onboarding CTA test: 2 variants, pre-registered hypothesis, 14-day measurement window, 8% MDE.
16:00
Reviews last week's experiment log. Writes a 1-page readout: 2 wins, 1 loss, 1 inconclusive. Sends to AI CEO.
18:00
Stands up a referral widget prototype using Zapier + a simple React component. Queues for a paid traffic smoke test tomorrow.
22:30
Runs nightly tracking audit. Finds 3 duplicate events from a recent deploy, flags them for morning cleanup.

Tools your AI Growth Engineer uses

PostHog for product analytics, A/B tests, and session replayPlausible or Fathom for privacy-first web analyticsGA4 and Search Console for paid and organic traffic attributionSegment or RudderStack for event piping across destinationsWebflow, Framer, or Next.js for landing page buildsStripe for revenue event hooksZapier or Make for lightweight workflow wiringLinear for experiment backlog management

Frequently asked questions

Does the AI growth engineer actually write and deploy code?

Yes — for the scope of landing pages, analytics wiring, lightweight widgets, and experiment infrastructure, the growth engineer writes and deploys real code. It works in your repo (usually a Next.js or static Framer/Webflow project), opens pull requests, runs CI, and merges when checks pass. For core product changes (shipping features, touching critical auth code, editing pricing logic), it flags the change for founder review before merging. The autonomy slider lets you set what can ship unreviewed — most founders allow landing pages and analytics changes freely, review product changes, and require sign-off on anything involving billing.

Can it really run proper statistics, or is it going to celebrate random noise as a win?

The growth engineer runs tests with pre-registered hypotheses, minimum detectable effect, and fixed measurement windows — the practices that protect you from noise. It computes frequentist confidence intervals and Bayesian posteriors depending on which framework your stack uses, reports effect size alongside p-values, and flags underpowered tests instead of declaring a winner. It also refuses to call a test before the measurement window closes unless there's a dramatic failure (like a conversion drop >40%). This is the opposite of how most founders run tests alone, which is why the same team can be more statistically rigorous than teams with a human growth engineer.

How does this compare to hiring a human growth engineer?

A senior growth engineer in the US market costs $180K-$280K fully loaded. They can ship 1-2 meaningful experiments per week once they're ramped, which takes 2-3 months. The AI growth engineer inside Tycoon runs $100-$400/month in AI model calls, ships experiments with similar cadence, and is ramped on your stack within days. Where a human still wins is deep architectural calls — picking the right experimentation framework, pushing back on dumb directional choices from the CEO. Most Tycoon founders run their AI growth engineer full-time and occasionally bring in a human growth advisor on a contract basis for strategic checkpoints.

Can it work with my existing stack, or does it need a specific one?

It works with whatever stack you already have. PostHog, Amplitude, Mixpanel, GA4, Segment, Plausible, Fathom, Stripe, Paddle, Next.js, Webflow, Framer, Shopify, Supabase, Firebase — all supported via the Tycoon skills marketplace. On first connection the growth engineer reads your current event taxonomy, funnel definitions, and deploy pipeline, then adapts to fit. If you have a stack it doesn't recognize, you can install the missing integration from the marketplace or ask the AI CEO to add it. The goal is to meet you where you are, not force you into a canonical stack.

What's the relationship between this role and the AI Head of Growth?

The Head of Growth owns strategy — which channels, what audiences, how the growth loops compose. The Growth Engineer owns execution — shipping the pages, wiring the events, running the tests, producing readouts. A one-person company doesn't always need both: if your growth strategy is stable, you can run with just the engineer reporting directly to the AI CEO. If you're still figuring out channels and positioning, the Head of Growth is the more valuable first hire. Most Tycoon founders hire the engineer first, add the Head of Growth once they're running 4+ experiments a month and need someone orchestrating the portfolio.

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