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Product.ai is the verified truth layer for shopping. The fleet built for SimplyCodes verifies codes across stores, turning guesses into verified claims and funding the business.
You will help scale this fleet responsibly, with a strong emphasis on correctness and cost control. Based in Santa Monica, Los Angeles, this hands-on role focuses on fleet economics, instrumentation, and owning key metrics inside Cortex, the company’s AI brain.
Product.ai is the verified truth layer for shopping. When a person or an AI agent needs to know what is actually true about a purchase, we answer with proof. SimplyCodes is our first proof at scale, the code verification service that shows shoppers the codes that actually work instead of a wall of dead ones. It earns about $22 million a year at roughly 60% margins. We are 100% founder-owned, profitable, and bootstrapped since 2009. No outside investors, no board. Fewer than twenty operators, outbuilding companies 10x our size.
When SimplyCodes tells a shopper a code works, a machine should have proved it. A checkout robot went to the store, added an item, applied the code, and watched what happened at the cart. You own that fleet.
Human spot-checking cannot reach the long tail, so the fleet is now the primary producer of the verified checkout evidence this company sells. Today the robots reach only a fraction of the stores beyond the big standardized platforms. Your job is to multiply that reach across hundreds of thousands of stores. Every store you add turns a guess into a verified claim. That $22 million is the engine that funds everything here, and those claims keep the merchant pages behind it worth ranking and trusting. The same proof is now a paid product for machines. Anonymous API access closed August 15; legacy free codes retire September 30.
One boundary. This seat proves the code works, with robots at real carts. A sibling posting owns the commerce-data supply chain, meaning what enters the system and how fresh it stays.
Agents write most of the code here, so the scarce thing is judgment, the design taste that keeps a fleet correct and cheap while it multiplies. You decide what to build and how you will prove it holds, with a high technical bar underneath.
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. Handed a checkout flow you have never seen, you can sketch the three ways it will break before you write a line. You see the platform family behind a one-off store, and the shared recipe behind a hundred one-off stores. When a robot fails at 2 a.m., your first question is structural: what class of store did we just discover?
You move fluidly between architecture and shipped code; a classifier design in the morning can be a deployed test by night. You treat agents as leverage you verify, not autocomplete you trust, which means you can point at a system you shipped, name the hardest failure you personally diagnosed in it, and say what you changed. You can do this job by hand and prove it, and that mastery is what lets you trust, or reject, what an agent hands back. A redo cycle costs far more here than compute does.
This is wrong if you guard a single lane and call the rest someone else's department. You own the fleet across automation, classification, infrastructure, and cost, and "that's not my job" ends the conversation. It's wrong if you pick technologies for how they'll look on your next resume rather than for what the fleet needs tonight. Wrong if you optimize for influence over output and dress hard news in stakeholder euphemism. Wrong if you wait to be told what to test instead of reading the system and deciding. And wrong if your code is whatever the model handed you and you couldn't say why it's right, or if you're comfortable letting an agent grade its own work. You'll be happiest here if your idea of craft is a fleet of robots that proves, store after store, that a code is real.
We don't run traditional engineering interviews.
1. Async video screen. Brief and on your own time — about fifteen minutes. We want to see how you think, not how you present.
2. Calls with company stakeholders. Short conversations with the people you'd build beside.
3. Conversation with the founder. How you reason about coverage, cost, and truth at fleet scale, and where you push back.
4. Paid work trial. A paid four-day engineering trial — real work, in our real environment, shipping to our real platform. We watch how you get grounded in the system, whether you write the spec before the build, how you verify what your agents produce, and whether your self-assessment is honest. We both learn more in four days than in forty hours of interviews.
Total first-year comp: $380,000 – $475,000 — base, plus performance-based ownership and profit-share programs. Base: $250,000 – $310,000, top of market for senior engineering.
Eligibility for the company's ownership and profit-share programs — grants are performance-based, with terms discussed at the offer stage. We cover 100% of family insurance premiums. Your token budget is effectively unlimited, steered by return, never capped. The model is built to mint partners.
Based in Santa Monica, Los Angeles — in person, five days a week. The rooms are real rooms. Relocation support available for the right builder.