Software Engineer, Verification Fleet

The hands of the truth engine - the checkout robots that test whether a code actually works, at fleet scale across half a million stores.

Product.ai is the verified truth layer for shopping - the intelligence that tells a person, or an AI agent, what is actually true about a purchase. 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. A small team, fewer than twenty operators, outbuilding companies 10x 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

When SimplyCodes tells a shopper a code works, we want a machine to have proved it - a checkout robot that went to the store, added an item, applied the code, and watched what happened at the cart. That fleet of robots is the seat.

Today those robots can test checkout at 21% of the merchants that don't run Shopify. The target is past 80% across roughly 500,000 stores. Every store you add is a claim we can verify instead of guess, and coverage is exactly what an AI agent pays us for when it asks whether a code is real.

"Builder," deliberately. Agents write most of the code here, so the scarce thing is judgment - the taste to design a fleet that stays correct and cheap while it grows past 80%. You decide what to build and how you'll prove it holds, with a high technical bar underneath. Your leverage is judgment and taste, not keystrokes.

The System You'll Need to Model

• The checkout maze. Every e-commerce platform breaks differently. The code field hides behind a different click on Shopify, Magento, WooCommerce, BigCommerce, and a thousand custom carts. A classifier has to sort roughly half a million merchants into 50 to 80 platform families, so one robot recipe covers thousands of stores instead of one. The maze is the problem; the family is the leverage.
• Fleet economics. A robot that costs more to run than the commission it protects is a loss. Every check spends compute, proxies, and time. You are always trading how often you re-test against what the test is worth - the fleet has to earn its own keep, store by store.
• The coverage-accuracy tradeoff. Pushing coverage from 21% toward 80% means testing messier, stranger stores where a robot is likelier to misread the cart. More reach and more certainty pull against each other, and holding both as you scale is the whole game.
• Evasion versus detection. Stores and their anti-bot vendors do not want to be automated. Fingerprinting, rate limits, and challenge walls move constantly. You navigate them without breaking the store or the law, and the ground shifts under you every month - this is a moving contest, not a fixed integration.
• Verdicts need tests; tests need verdicts. You build beside the seat that owns the scoring science - the machine verdicts that decide what we claim is true. Those verdicts are only as good as the checkout evidence your fleet produces, and your fleet only knows what to test because their scoring shows where the truth is thin. Two seats, one loop.
• Cortex, the brain you build inside. You work inside Cortex - the shared AI brain that runs the company and is the product family we sell. Every operator works through governed AI sessions, and the substrate answers its own questions from more than 8,600 documents. You are not using AI on the side; you are building inside the thing we sell.

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

What You Will Own

• The robot fleet, end to end. The browser automation that drives real checkouts, the classifier that sorts ~500K merchants into 50 to 80 platform families, and the scheduler that decides which stores to test and when. Agents write much of the code; you own the design, the failure modes, and the verdict on what ships.
• Coverage as your number. Machine-tested checkout today reaches 21% of non-Shopify merchants; you own the line from there past 80% across roughly 500,000 stores. This is the number an AI agent is really buying when it decides to trust us.
• Fleet economics. Cost per verified checkout, held below the commission each check protects. You make the spend legible and make the fleet earn its keep, store by store, rather than making it small.
• Anti-bot navigation. The evolving contest with fingerprinting, rate limits, and challenge walls - navigated at scale without breaking the store or the law.
• The instrumentation that proves it. Dashboards and ledgers that show, for any claim, when it was last tested, whether the robot really reached the cart, and what the check cost. Correctness you can watch, not correctness you assert.
• The number, co-signed. Within your first quarter you co-sign a seat charter - the model we run for senior operators. It names one machine-checkable number that proves the seat works (machine-tested checkout coverage is the obvious one) and writes down what you decide freely versus what you propose for the founder to sign. You own a number, not a backlog.

Who You Are

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 - and you are as comfortable deciding what to build as how. You treat agents as leverage you verify, not autocomplete you trust: 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 exactly what lets you direct agents and trust - or reject - what comes back. The expensive thing here is a redo cycle, never the compute.

You have built browser automation, web scraping, or crawling systems at real scale, and you operated them in production - you know what a fleet of headless browsers does to your infrastructure bill and your on-call sleep. You have reverse-engineered a site that did not want to be automated, and won. Playwright, headless Chrome, proxy rotation, and queue-backed job systems are familiar ground; Node.js and Python are daily tools. We care about the artifact and the reasoning far more than where you did it - no degree to check, no pedigree to clear.

Who this isn't for. 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 read on your next resume rather than for what the fleet needs tonight. It's wrong if you wait to be told what to test instead of reading the system and deciding. And it's 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 quietly proves, store after store, that a code is real.

How We Evaluate

We don't run traditional engineering interviews.

Async video screen. Brief and on your own time - five to six questions, about fifteen minutes. We want to see how you think, not how you present.

Calls with company stakeholders. Short conversations with the people you'd build beside.

Conversation with the founder. How you reason about coverage, cost, and truth at fleet scale, and where you push back.

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. This is demonstrated performance on work-relevant tasks, the only signal we trust.

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

Compensation & Ownership

Total first-year comp: $380,000 - $475,000 - base, plus real ownership, plus profit sharing. Base: $250,000 - $310,000, top of market for senior engineering.

You join as a partner, not just an employee. Profits Interest Units (PIUs) at a $0 strike give you real ownership from day one, taxed as capital gains. You share pro-rata in the free cash flow the company generates each year, and you can sell into an annual tender for real liquidity - actual dollars, not paper you wait a decade to touch. 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.