Machine Learning Engineer, Verification Engine

Why This Role Exists

The verification engine answers the question of whether a claim is true at scale. Currently, machines settle 40% of verdicts and humans settle the rest. The goal is to increase the proportion settled by machines to 80% while maintaining 95%+ accuracy against a human-graded exam set the model cannot see or game.

You Will Own

• The scoring science. The machine verdicts that decide what we claim is true about shopping — the models, the thresholds, and the rulebooks behind every automated verdict.

• The confidence-scoring pipeline. How sure the machine is, per claim class, with the decay each class carries. You own its calibration and its honesty: a verdict that claims 95% and is right 70% of the time is a bug, and it is yours.

• The golden-set exam. The human-graded exam the model cannot see or game. You grow it, you keep it honest, and you give it teeth. When a verdict is disputed, the exam decides — not the loudest engineer in the room.

• The arbitration rulebooks, co-owned. The rules that settle a contested verdict, authored with our truth scientist. Two owners by design: the truth an entire company ships through does not rest on one person.

• Your seat charter. Within your first quarter you co-sign a charter for this seat — one machine-checkable number that proves it is working, and a written split of what you decide freely versus what you bring to the founder to decide.

Who You Are

• Able to interrogate every green checkmark. A passing eval is a claim, and claims get challenged — you ask what the test could not have caught before you ask what it confirmed.

• Treats agents as leverage you verify, never as an oracle you trust. Writes clearly, because clear writing is evidence of clear thought.

• Moves between a confidence-tier scheme in the morning and the verifier fleet that enforces it by evening without getting stuck at either altitude.

• Has high agency, thinks in corpora, and wants your verification to be the reason an entire truth layer can be trusted.

Who This Isn't For

• Optimizes for leaderboard scores or trusts vendor-reported evals.
• Optimizes for output quality rather than verifying it.
• Optimizes for tightly-scoped tickets and lanes.
• Optimizes for accepting agent-produced findings without questioning them.

Compensation & Ownership

• Total first-year comp: $400,000 – $500,000 (base + ownership + profit sharing).

• Base: $260,000 – $330,000 — top of market for machine learning engineering.

• Ownership is real and liquid. Profits Interest Units (PIUs) — Class B Membership Interests at a $0 strike, real ownership from day one, with capital-gains treatment.

• Annual pro-rata profit sharing from free cash flow — real cash every year, not a promise tied to a distant exit.

• An annual tender offer buys back vested interests at fair market value, so you can turn ownership into cash every year without waiting for an IPO.

• 100% premium coverage for you and your family.

• The token budget is effectively unlimited, steered by ROI and never capped — high usage is encouraged, because the harness tracks what the spend returned.