This is an Account Executive, Enterprise seat selling AI-detection infrastructure to everything outside universities — law firms, accounting and consulting firms, tech companies, payments companies, comms teams, and frontier AI labs. It pays $175K–$200K+ in base salary against $350K–$400K+ on-target earnings, plus meaningful equity at a company that just raised its Series A, with valuation up roughly 5x in nine months. You’d be the second enterprise seller, reporting to the VP of Sales, joining a rep who is already well ahead of quota in a segment that only began in January 2026. The motion is mostly inbound to start — already more than the team can handle — with roughly 20% of your number eventually coming from pipeline you source yourself. It’s based in New York, three days a week in the office, and it’s open now — confirmed October 22, 2026.
Not the right location or comp? See the other roles I’m filling.
This is the second seat open at this company right now. If selling to universities is more your lane than law firms and tech companies, there’s a separate Account Executive, Education seat on the same product, same comp range, same New York office.
Every professional services firm now has a question it doesn’t want its clients to ask.
Law firms are running their associates’ client deliverables through this product before anything goes out the door. The stated reason is hallucinations. The real reason is billable hours. If a client finds out a brief was drafted by a model and still billed at the full rate, the conversation turns to fees. So firms check quietly, before anyone else does. A Big Four accounting and consulting firm is doing the same.
Then the other side showed up. A publicly traded company asked to check its outside law and accounting firms, to see how much of what it pays for is written by AI.
Professional services is only one pocket. A large payments company is rolling it out across its entire organization because its CEO got tired of AI-written material landing in his inbox. Other companies use it to see how much of their internal work and communications are AI-generated. Comms teams check executive quotes before they go public. Frontier AI labs and data-labeling companies run it at API scale to keep AI-generated slop out of their training data. Those are large API deals.
Underneath all of it is the most accurate detector in the category, with a false-positive rate of about 1 in 24,000. It was built by Stanford ML researchers with time at Google and Tesla, and it’s trained to catch text that has been paraphrased or run through “humanizers” to beat detectors.
Why is this different from a typical AE seat?
Three things, all structural — the demand already exceeds capacity, the proof is already there, and the buyer is unusually varied.
Demand already exceeds capacity. A full New York Times write-up while the company was still seed-stage sent inbound past what the sales team can work. You’re not generating interest from nothing — you’re helping a team that already has more demand than it can answer.
The first enterprise seller already proved it out. The current enterprise AE is well ahead of quota in a segment that only began in January 2026. You’re the second seller into a motion that’s already working, not the first to test whether it does.
The buyer is unusually varied. Unlike a typical enterprise seat with one defined ICP, this territory spans law firms, accounting and consulting firms, tech companies, payments companies, comms teams, and AI labs — some of the largest deals come from API usage at AI labs, not from a classic enterprise sale.
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How does this compare to a typical founding AE seat?
Less risk and a more proven motion than a founding seat — in exchange for a segment whose shape is still being decided. The sale is already working; your job is to help scale it, not invent it. For the wider checklist, what good founding AE roles look like is the frame I use, even for a role like this one that isn’t a founding seat.
| Typical founding AE seat | This role | |
|---|---|---|
| Proof it works | Founder-led deals, often unproven at scale | First enterprise AE already ahead of quota, fresh Series A, valuation up ~5x in nine months |
| Your risk | Product-market fit may not exist | Fit is proven; the risk is an undefined segment and a playbook still being written, not demand |
| Demand on day one | Zero | Heavy inbound already flowing, more than the team can work |
| Comp | Often $100K–$175K base | $175K–$200K+ base, $350K–$400K+ OTE, plus meaningful equity |
| Motion | You build the playbook from scratch | Mostly inbound to start; ~20% self-sourced as you grow the number |
| Buyer | Varies | Everything outside universities — law firms, accounting and consulting firms, tech companies, payments companies, comms teams, AI labs |
Where is the upside — and where is the risk?
The upside: this is a company well past early traction, with a large self-serve business underneath it, and enterprise is the next leg of growth — one that’s already working. The current enterprise seller is well ahead of quota in a segment that only began in January. Inbound is more than the team can handle: a full New York Times write-up while the company was still seed-stage sent demand past what sales can work. And it’s a fresh Series A, with valuation up roughly 5x in nine months, which makes the equity in this offer a real part of the package rather than a rounding error.
The risk: enterprise is still a catch-all here. The company hasn’t decided whether to split the segment by vertical or by use case, so you might talk to a tech company at 10 and a law firm at 11. The playbook is being written while you sell — if you need a defined ICP and a script on day one, this is the wrong seat. And the underlying model is in an arms race: every new model and every new “humanizer” tool built to beat detectors is a fresh test the product has to keep passing.
Who is this role actually for?
A closer who’s comfortable selling to more than one kind of buyer in the same week — a tech company at 10am, a law firm at 11 — and who’s energized rather than unsettled by a segment whose territory and playbook are still being defined. It takes someone who can work heavy inbound efficiently while still building roughly a fifth of their own pipeline.
Who it isn’t for: someone who wants a single, defined ICP and a proven script from day one, or someone who needs the territory lines drawn before they’ll commit.
What would I push on before signing?
How the enterprise territory gets shaped once there are two sellers. With the segment still undefined, it’s worth asking whether the split will be by vertical, by use case, or by inbound rotation, and how existing accounts are handled when the team grows from one seller to two.
If you’ve sold to more than one kind of buyer in the same week and liked it, talk to me.
