AI Engineer

System You'll Need to Model

• Fleet of production automations whose failure mode is silent death.
• Pipelines rarely fail loudly — they stop, and the cost accrues invisibly until someone notices days later.
• The real engineering problem is liveness: designing alarms and deterministic checks so that no automation in the fleet can die unnoticed.
• Long-lived agent runs that go 1-4 hours unattended.
• They hold together not because someone watches them, but because they run on architectural law (the rules an agent run is bound by), a fuel budget, and verification built in.
• You design what governs a run, what it's allowed to spend, and what proves it worked.
• Verification the agent cannot author.
• Architecture is external truth anchors, regression corpora, and oracle-separated checkers — a separate judge that never sees what the builder saw.
• This separation is the whole game, and it's also the company's thesis: verified truth a model can't fake.
• Token spend judged by what it moved, not what it cost.
• Every run is instrumented for the outcome it produced, and budget gets redirected toward what's working while the run is still going.
• We're quality-maximalist: the expensive thing is a redo cycle, never tokens.
• Cortex — the shared AI brain you work inside.
• It isn't a tool you reach for now and then; it's the operating system that runs the whole company, and it's the same intelligence family we sell to the world.
• Every operator here works through governed AI sessions on it, and it answers its own questions from a base of 8,600+ documents.
• The automations you build stand on it, feed it, and are bound by the same architectural law your own work is.
• Nobody else runs their company on the product they sell.
• Architecture that moves weekly.

Who You Are

• Form working models of running systems on your own.
• Read a pipeline you've never seen and sketch its failure modes the same day, notice where your model is wrong, and update fast.
• Write clearly, because clear writing is evidence of clear thought — and here, what you write becomes the law your agents execute.
• Move between architecture and implementation without getting stuck at either altitude.
• Design a verification gate in the morning, ship it alarmed and instrumented by the afternoon.
• Have strong, earned opinions about guardrails, evaluations, and agent behavior, and make good calls in the gray.
• High agency is your resting state.
• Built automations that ran in production for months, and can say precisely how you knew when one was failing.
• Evaluation harnesses, CI gates that actually block, data pipelines with verification companions, agent systems with regression suites.
• Treat agents as leverage you verify, not autocomplete you trust — depth is what lets you trust the verdict.

Who this isn't for

• Someone who works best as a watched assistant or wants a ticket queue to execute and report done.
• Someone who mostly optimizes for logos on a resume.
• Someone who thinks mainly in projects, timelines, and programs — the steering here happens in real time, mid-run.

How We Evaluate

• Video screen.
• Brief and async: 5-6 questions, about 15 minutes total, done whenever works for you.
• Calls with company stakeholders.
• Conversation with the founder.
• Chemistry and comprehension — can you model the system you just read about?
• Paid work trial. A paid one-to-two-week engagement — real work in our real environment.

Compensation & Ownership

• Total first-year comp: $375,000–$450,000 (base + equity + profit sharing).
• Base: $250,000–$300,000.
• We set the number from what you've actually built, and every hire ships from day one.
• Profits Interest Units (PIUs) — Class B Membership Interests at $0 strike, real ownership from day one, capital-gains treatment; annual pro-rata profit sharing from free cash flow; annual tender liquidity; 100% family premium coverage; and an effectively unlimited token budget, steered by ROI, never capped.
• This is a partnership, not a salary line.