We're building at the frontier of what applied AI can do inside real work — the operational core of an industry that moves the physical economy. One of the largest on earth, essential, and almost entirely untouched by modern AI. It still runs on people, spreadsheets, and software written before the internet.
We went vertical first on purpose. Vertical is where the hard problems live: messy inputs, real consequences for being wrong, decades of institutional knowledge nobody wrote down. Anything that works here has been tested against reality in a way horizontal tooling never is.
And it doesn't stay vertical. The systems we're building — how work gets decomposed, verified, corrected, and learned from — aren't specific to one industry. They're specific to work. The vertical is the proving ground. The reapplication is the company.
We're deliberately quiet about which industry until we talk. What we'll say now: it's enormous, it's overlooked, and the incumbents aren't coming. You'll get the full picture on the first call.
A cap table most early-stage companies would envy. Raised at the top quartile of seed-stage rounds by size — 36+ months of runway — from the seed investors who were early in Palantir, Databricks, Anduril, GitLab, Retool, Lyft, Square, DoorDash, Superhuman, and Ironclad.
Real traction, right now. Live in production with design partners, and the data flywheel is already turning. Demand isn't the bottleneck — execution is. The market is tens of thousands of enterprises, each worth seven figures a year.
Founded by multi-time exited operators. The founding team has built and sold multiple software companies and spent years up close with dozens more. You're joining people who know how this is actually done — not learning it alongside them.
You're the founding engineer for AI. You own the intelligence layer end to end — the reasoning, the learning loop, the reliability — and as we grow, you build and lead the AI team beneath you.
The CTO owns the platform you stand on: data layer, connectors, infrastructure, security. You own everything that makes the product think.
The architecture isn't inherited, and there's no senior AI peer above you. You set it. That's the rarest thing this role offers and the reason it has to be the right person — you need to be entrepreneurial, the kind of engineer who ends up founding something. We'll go deep on the architecture when we talk.
The bar is AI judgment, not industry tenure. You don't need to know our domain — we'll teach you. What you can't fake is being native to this field while it's still being invented.
You've deployed applied AI in production. (Hard requirement.) You've taken an LLM system from prototype into real users' hands, where being wrong had real consequences, and owned what happened next. Ideally more than once.
An applied-LLM systems builder, not a model researcher. If "AI engineer" means fine-tuning, RAG, and embeddings to you, this isn't the role. We're looking for the Simon Willison / Hamel Husain / Eugene Yan school.
You live in evals and guardrails. You know how to bound a system you can't fully trust, and prove it's safe before it ships.
Intellectually honest about the limits of your own systems. You volunteer failure modes unprompted. We weight this as heavily as raw capability.
You want to build the team, not just the system. Within a year, this role is hiring, setting standards, and multiplying itself.
Anthropic · AWS Bedrock · Python · Burr · LangGraph · ReAct
Know someone exceptional? Introduce us. If we hire them into a full-time role, we'll send you $5,000.
