AI Product Builder (Martech)

Someone who has worked inside marketing: ad platforms, creatives, funnels and attribution are familiar, and you understand why hourly campaign optimization matters.

A product person who speaks in problems and outcomes, not shipped features. You frame every roadmap item as a bet on a metric and can name the metric behind your last three launches and what it did. You kill features. You talk about money as easily as about UX.

An AI builder, self-directed. You have built something alone in the last six months with Claude Code, Cursor or LLM APIs, and it is in use. A v0 takes you hours, not sprints.

Data fluency. You pull your own numbers (SQL, Amplitude or similar), read a retention curve, design a clean experiment, and know noise from signal.

Proactively identify and implement new martech solutions before they become bottlenecks — shaping the long-term vision of the domain and driving technical and product priorities.

Learn how the marketing and growth teams actually work and find the bottlenecks they haven’t named yet.

Choose how each problem gets solved — build it with AI, buy it, integrate it, or change the workflow — and define the MVP and the success metric before the first line of code.

Scale what marketing can do without scaling the team: automate the operations that cap how many tests, creatives and campaigns we can run, and measure the effect in hours saved and ad performance gained.

Connect the pieces into a pipeline — research → creative production → launch → optimization — replacing manual hand-offs with automation.

Own what you ship after launch: adoption by the teams, reliability in production, iteration on real usage.

Practical martech, performance or growth marketing experience; you’ve worked with ad platforms and their APIs.

Product experience in Marketing B2B SaaS or in-house martech teams: validated problems, measured impact.

5+ years in product and/or engineering, with hands-on experience building production services.

Daily use of AI-assisted coding (Claude Code, Cursor, Codex or similar) to ship solutions, not just prototypes.

Full-cycle autonomy: discovery → design → build → launch → measure, with minimal direction.

Experience at mobile app publishers (consumer subscriptions with high-volume user acquisition).

Meta Marketing API and Google Ads API.

LLM pipelines on data: classification, tagging, enrichment.

Node.js/TypeScript or Python; deployment on Railway or similar.