Senior Software Engineer

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 own the product surface and the systems beneath it — the layer where AI-generated work meets the human who has to stand behind it.

That layer is the whole ballgame, and it's badly underexplored. Nobody has good answers yet for how a person supervises a system that's right most of the time: what you surface, what you let through, what you make someone look at, how you show your work well enough to be believed. You'll be inventing that, not implementing it.

• The product surface. Where work gets reviewed, corrected, and approved. This is where the product either earns trust or doesn't.

• The services underneath it. State, jobs, queues, retries, and the boring-critical machinery that makes probabilistic output durable rather than a nice demo.

• The domain model everything stands on. Get the core objects wrong early and every team pays for it for years.

Not a feature-factory seat. The team is small enough that you own surfaces end to end — schema to pixel — and close enough to customers that you'll hear directly when you got it wrong.

The bar is judgment and range. You don't need to know our industry — we'll teach you the domain.

• You've built product end to end at an early-stage company. (Hard requirement.) Schema through interface, in real users' hands, where you owned what broke. Big-company depth in one narrow layer isn't the profile.

• Strong in Python, and fast in an unfamiliar codebase. You don't need every line of our stack on your résumé — you do need to be dangerous in it inside a month.

• You're comfortable building around systems you can't fully predict. Probabilistic output fails in ways CRUD apps don't. If that reads as an interesting design constraint rather than someone else's problem, keep going.

• You have product opinions and you argue for them. You want to watch people work and push back on a spec that doesn't survive contact with reality.

• Intellectually honest about the limits of your own systems. You volunteer failure modes unprompted. We weight this as heavily as raw capability.

Python · FastAPI · Celery · PostgreSQL · Redis · AWS · React

Know someone exceptional? Introduce us. If we hire them into a full-time role, we'll send you $5,000.