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Product.ai, based in Santa Monica, CA, seeks a Revenue Architect to own the billing and metering spine for the company’s paid API ecosystem. You will oversee the commission engine, attribution, and reconciliation for around $22M in annual revenue, and drive the cutover with a focus on correctness and auditable money flows.
The role blends system design with hands-on execution, ensuring machines and humans trust the invoices and the ledger.
Build the metering and billing spine that turns a free API into paid revenue. Own the ledger behind roughly $22M a year that already flows.
Product.ai is the verified truth layer for shopping: what is actually true about a product, including when not to buy. SimplyCodes is the first proof at scale, the code verification service that shows shoppers codes that actually work, earning around $22M a year in revenue at roughly 60% margins. The company is 100% founder-owned and bootstrapped since 2009, with no outside investors and no board. Fewer than twenty operators outbuild companies ten times our size.
You will own two revenue engines. One pays for everything today; the other we are switching on now.
The first engine already works. Hundreds of thousands of stores and millions of shoppers, real money moving every day. It funds everything else we build. A few engineers and the founder run it, and it has far more upside than its owners have hours.
The second engine barely exists. Our developer API goes paid, which means AI agents and outside developers buying verified-commerce data by the key. Paid keys need a billing spine: metering, quota, spend caps, tiered plans, and invoices a customer can trust. Today that spine has no owner.
This seat owns both. You steward the money that already flows, and you build the money that is about to. A revenue architect owns what we sell, to whom, and at what price. This seat owns whether the meter, the ledger, and the invoice are right, and has final say on the money path's architecture. You are measured in dollars.
If reading that energizes you, keep going. If it feels overwhelming or underspecified, this isn’t the right fit.
You reason in invariants, failure modes, and tradeoffs, and you tie each one to the dollar it moves. You can read a system you have never seen well enough to sketch where it leaks money the same day. When a number looks wrong, you instrument the pipeline and find the mechanism. You chase reconciliation drift to a root cause instead of learning to tolerate it as noise.
Agents are your production system. You direct them and you verify what comes back. You can do this job by hand and prove it, and that mastery is exactly what lets you trust or reject what an agent hands you instead of taking it on faith. The expensive thing here is a redo cycle; compute is cheap.
You have owned a system where the outcome was real money: payments, billing, metering, an affiliate or commission ledger, attribution, fraud scoring, or a marketplace transaction path at scale. You can point at it and explain the mechanism that moved the number instead of just showing the chart. You know why idempotency keys exist because you have paid for their absence. Where you did it and what you studied count for far less than that you built the money path and watched it hold under load. That is the transfer we want.
This seat is wrong if you stay in one lane and call the rest someone else’s department. It is wrong if you measure yourself by features shipped instead of the revenue those features move, or if you want a ticket queue and a finished spec rather than a number to own. It is wrong if you need a brand‑name logo on your resume or a platform team beneath you to feel senior, and wrong if you build influence and consensus instead of output. And it is wrong if you are comfortable shipping what an agent produced without being able to say why it is right. You will be happiest here if you want the whole money path yourself and want to be measured on what it produces.
We don’t run traditional engineering interviews. We evaluate demonstrated performance on work‑relevant tasks, in four steps.
Total first‑year compensation: $400,000 – $480,000 (base plus performance-based ownership and profit‑share programs). Base: $280,000 – $330,000 — top of market for the work.
Beyond base: eligibility for the company's ownership and profit‑share programs — grants are performance‑based, with terms discussed at the offer stage; 100% family premium coverage; an AI tooling budget steered by return, never capped.
This structure is built to mint partners. When the company wins, you win — in real, liquid dollars, every year.
Based in Santa Monica, Los Angeles — in person, five days a week. The rooms are real rooms.