Founding Product Lead

You own the consumer quality bar across every surface. You turn strategy into the locked specs our AI agents build from, and you call the verdict on what ships.

Product.ai is the verified truth layer for shopping — the intelligence that tells you what's actually true about a product, including when not to buy. Our first proof at scale is SimplyCodes, the code verification service: roughly $22M in revenue at ~60% margins. Profitable. Bootstrapped since 2009. No outside investors. No board. A small team — fewer than twenty operators — outbuilding companies 10× our size.

Strong people find us and keep finding us — they apply over months and years, because the field moves fast and the exact profile we need moves with it.

Why This Role Exists

You will author more of the shipped product than anyone except the founder. Our founder owns product strategy — that doesn't change. What's open is the seat directly below it: you hold the consumer quality bar across every surface — chat, web, extension, mobile, personalization — and you turn strategy into the specs our agents build from.

A spec here is not a document that drifts out of date. A locked spec is the roadmap-of-record. Agents build directly from it. Write the spec, and the product follows.

This is a founding individual-contributor role, not a VP role with a PM org to build. You direct agents and work beside a handful of elite operators. One thing has to be true: you are the best spec author in the company. In a system where anyone who can write a clear spec can ship, the work flows to whoever writes the clearest one — this is a meritocracy of the written artifact, and you want it that way. Your leverage is judgment and taste: what to build, and how you'll know it worked. Not headcount.

The System You'll Need to Model

• Knowledge-graph products. Three graphs, one answer: a Commerce Graph (what exists, prices, availability), a Truth Graph (product claims that survive our verification research and carry citations), and a Preference Graph (what each user actually cares about). The Preference Graph is the newest of the three — you will likely own its birth, and it is a preference-modeling problem as much as a catalog problem. Every product decision is a decision about how three graphs combine into one trustworthy answer.
• Trust-calibrated conversational products. General assistants average the internet confidently. Ours answers with verified truth and citations — including "don't buy this." The bar isn't engagement; it's users trusting the verdict on a high-stakes purchase. Calibrated trust is a harder product problem than delight.
• Agent-mediated distribution. The product has to be a named capability inside ChatGPT, Apple App Intents, Gemini, and Claude — surfaces where an AI agent, not a human, decides whether to call us. Being chosen by agents is the new being indexed by Google, and you'll write that playbook while you ship it.
• Spec-driven agentic development. Intent becomes a visual mockup, then a locked spec, then agents build, then separate verifier agents check the build against the spec. The verifier is the load-bearing part: it's a test the building agent can't author or grade, so the system can't quietly approve its own work. Your spec is the interface the whole pipeline builds and grades against. Your taste gates what ships.
• Cortex, the company that compounds. You'd work inside Cortex — the shared AI brain that runs the company and is the same product family we sell. Every operator works through governed AI sessions, and our agents answer their own questions from a shared substrate of 8,600+ documents. Nobody else builds this way. You need to model where the product is heading and write specs that anticipate direction — reading the system ahead of the brief rather than waiting for one.

If reading that energizes you, keep going. If it feels overwhelming or underspecified, this isn't the right fit.

What You Will Own

• Product law. The locked specs agents build from, across chat, web, extension, mobile, and personalization. Agents write the code and content; you write the spec, the acceptance tests, and the verdict on what ships. This is the discipline of treating the spec as the build interface for AI agents — practiced here as the daily production system, not a pilot.
• The consumer quality bar. The standard for everything a shopper touches, held as evidence tests for four to six product outcomes — each falsifiable, each with a test a stranger could run. Quality-bar ownership and evaluation design are the transferable craft; our surfaces are the instance. When the bar and the schedule conflict, you hold the bar.
• The decision-shaped UX standard. With our Founding Designer, you'll define what a verdict looks like: interfaces shaped around the decision the user is making — buy, wait, walk away — rather than around the content we happen to have. Communicating calibrated confidence (when we're sure, when we're not) is the core interaction problem, and it is decision-support UX at its hardest.
• Outcomes, personally. One or two falsifiable product outcomes a quarter that you run end-to-end yourself — intent to locked spec to verified build. Visibility here is decisions registered and outcomes moved — the work speaks for itself, not the hours behind it.
• The operator contract. This is the model we run: in your first quarter you co-sign a seat charter — one machine-checkable number that proves the seat works, plus a written split of what you decide freely and what you bring to the founder. The seat is defined on paper, then you own it.

Who You Are

You independently form working models of complex systems, notice where your model is wrong, and update fast — including killing your own ideas when the evidence says so. You don't need perfectly defined scope to start; you need enough signal to reason from first principles. You write clearly because clear writing is evidence of clear thought, and here your writing is executable.

You move between strategy and locked spec without getting stuck at either altitude — from "what should chat do when the evidence conflicts?" to a spec agents can build from, same day. Agents are your production system, 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 — the verdict an agent hands you. You treat the agent's output as something you check, not something you accept on faith. The expensive thing here is a redo cycle, never the compute.

You've shipped consumer products people actually use — live surfaces, not strategy decks — and written specs precise enough that someone, or something, built the right thing without a meeting. The craft you must already own: spec authorship, consumer product judgment, and evaluation design. The experience we accept as comparable: commerce, marketplaces, search, or conversational products. What you'll grow into here: directing agent fleets as a production system, verification design, knowledge-graph product architecture, and distribution through agent platforms. We care about the artifact and the reasoning more than where you did it.

Who this isn't for. This role is wrong if your product practice is roadmap theater — decks, alignment meetings, and planning rituals that never touch the build. It's wrong if you need an engineering team to hand you velocity, or a PM org beneath you to feel senior. It's wrong if what you're chasing is a VP title and the team-building that usually comes with it — here you direct agents and a handful of elite operators, and your judgment in writing is the thing that ships. It's wrong if you're comfortable shipping what an agent produced without being able to say why it's right. You'll be happiest here if you want to own the whole product loop yourself and be measured on what it produces.

How We Evaluate

We don't run traditional product interviews.

1. Video screen. Brief and async — about 15 minutes. We want to see how you think, not how you present.
2. Calls with company stakeholders. Short conversations with key members of the team.
3. Conversation with the founder. How you model systems, where you push back, and whether you can hold the product bar in a live argument.
4. Paid work trial. A paid 10-14 day strategic trial — real work in our real environment, taking a live product problem from intent to locked spec to verified build. We watch four things: how you get grounded, whether you write the spec before the build, how you verify what your agents produce, and whether your self-assessment is honest.

If the work above reads like yours but your resume is unconventional, apply anyway. We hire on the work and the reasoning, not the pedigree.

Compensation & Ownership

Total first-year comp: $325,000 – $475,000 (base + performance-based ownership and profit-share programs). Base: $250,000 – $300,000 — top of market for product leadership.

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; and an effectively unlimited token budget, steered by ROI, never capped.

This is a partnership structure 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.