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Product.ai

Senior Engineer, Revenue Systems

Posted An Hour Ago
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Hybrid
Metropolitan, CA
250K-480K Annually
Senior level
Hybrid
Metropolitan, CA
250K-480K Annually
Senior level
Own and grow a profitable revenue engine: build durable server-side attribution, reconcile messy affiliate-network feeds into a single revenue ledger, keep real-time freshness across large-scale crawl budgets, and optimize spend versus revenue. Enable agent-first access and measure impact in dollars. Operate autonomously and diagnose revenue-leaking failure modes at scale.
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Own a profitable engine at a scale most builders never touch.

Product.ai is the verified truth layer for shopping - what's actually true about a product, including when not to buy. Profitable. Bootstrapped. No outside investors. No board. 20 people outbuilding companies 10× our size.

Why This Role Exists

There's a profitable revenue engine inside this company - hundreds of thousands of stores, millions of users, real money every day - and it funds everything else we build. (It's SimplyCodes, the largest independent coupon-verification platform on the internet.) A few senior engineers and the founder keep it running, each stretched thin; it has far more upside than its owners have hours. So you'd be handed something rare: a profitable engine nobody has time to push as far as it can go - and the autonomy to push it. You own its technical surface, and you're measured in dollars.

The System You'll Need to Model

  • Attribution that survives a hostile browser. Every dollar traces back to one fragile artifact - a click ID that has to survive an async gap of hours to months, across dozens of affiliate networks that each name and format it differently. Browser tracking is dying, so the durable path is server-side. Get this wrong and you don't slow a page, you misstate the revenue.
  • A revenue ledger assembled from messy feeds. Commission truth arrives from dozens of affiliate-network APIs, each with its own schema, lag, and reversal rules. Turning that into one correct revenue picture - append, reconcile, rank - is the job; a silent bug here doesn't crash anything, it quietly costs real money before anyone notices.
  • Freshness that is the revenue. Hundreds of thousands of pages, a zero-sum crawl budget, and promotion data with a half-life of hours - the real-time, branded-transactional content class models can't cache or fake, where AI is a tailwind, not a headwind. Stale pages leak ranking, ranking leaks traffic, and traffic is what the whole engine monetizes.
  • Spend you make legible. Compute is large by design - the expensive thing is a redo cycle, not the tokens. You read cost against the revenue it moved, not just keep it small.


Where the Engine Is Heading

The next distribution shift is agents choosing commerce tools instead of people. The push: make the engine callable by AI agents by default, and fold it into Product.ai's verified-truth platform - real but early, upside you'd own.

Who You Are

You reason in invariants, failure modes, and tradeoffs, tie each to the dollar it moves, and can read a system you've never seen well enough to sketch where it leaks revenue the same day. You can do this job by hand and prove it - that's what lets you direct agents and trust the result. The signal we want most: point at a system you owned where the outcome was real money - a ranking, freshness, attribution, or conversion change - and explain the mechanism that moved the number, not just the chart. Where you did it and what you studied matter less than that. If you've built attribution, fraud-scoring, ad-targeting, or search and marketplace systems at scale, that's the transfer we're looking for.

Who this isn't for. If you stay in one lane and call the rest someone else's department, measure yourself by features shipped over the revenue line, reach for a different model instead of instrumenting the pipeline when a number looks wrong, need a platform team beneath you, or can't say why the code an agent handed you is right - this isn't your seat.

How We Evaluate

We don't run traditional interviews.

  • Written artifact. Submitted with your application. Show us a system you built, the hardest failure you diagnosed and what you changed.
  • Video screen. Brief and async: 5-6 questions, about 15 minutes total, done whenever works for you.
  • Calls with company stakeholders. Short conversations with key members of the team.
  • Conversation with the founder. Chemistry and comprehension - can you model the system you just read about?
  • Paid work trial. One to two weeks of real work in our real environment.


  • Compensation & Ownership

    Total first-year compensation $400,000 - $480,000 (base + ownership + profit sharing). Base: $250,000 - $330,000.

    Profits Interest Units (PIUs) - Class B Membership Interests at $0 strike, real ownership day one, capital-gains treatment; annual pro-rata profit sharing from free cash flow; annual tender liquidity; 100% family premium coverage; effectively unlimited token budget, steered by ROI, never capped.

    Based in Los Angeles, California. Hybrid, with flexibility; for the right builder, we're open to remote.
    #BI-Hybrid

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