AI Automation Engineer

Therabot makes a robot that massages your legs. Behind the product is a company deliberately built to run on AI systems — and we are looking for the person who builds and runs those systems.

If you have been quietly building agents at night because you can't help yourself, and you have been waiting for somebody to hand you a real business to point that at: this is that job.

One hard requirement: we are specifically looking for people already building on Open Claw or Hermes
. Not "have heard of" — actually running agents on one of them today. If that is not you, this particular role is probably not the right fit.
Be intentional about applying. The application ends by asking what you have built on these platforms specifically — agents, skills, workflows, integrations you have actually run. Have that ready before you start.

Most work falls into three layers. Doing it. Reviewing it. Deciding on it.

Our view is that the first layer is increasingly machine work. The people here should be reviewing that work or making the call on it — and the better the systems get, the less of even that is needed.

1

Pulling the numbers, drafting the reply, reconciling the transactions, building the report. Repetitive execution — the layer you will be automating.

2

A short daily pass over what the machine did — is it running, is it right, is anything stuck waiting on a person. Minutes, not hours.

3

The handful of calls that genuinely need human judgment. These should arrive pre-framed: here are the three things worth your attention, yes or no.

Every business function we run — accounting, support, marketing, inventory, reporting — gets rebuilt to fit that shape. Your job is to do the rebuilding.

You will take a business function that currently eats a person's day, and turn it into a system that runs itself and asks for a human only when it genuinely should. Then the next one. Then the one after that.

• Build the systems. Agents, pipelines, scheduled jobs, and integrations across our real stack — Shopify, Meta, Google, email, accounting, and our own internal tools.
• Make them know when they are unsure. This is the hard part and the part we care most about. An agent that fails loudly is useful. An agent that fails confidently and quietly is worse than no agent at all.
• Build the trend layer. A snapshot that looks fine every week can be three percent wrong every week. Drift and silence are the two failures a human spot-check will never catch on its own.
• Turn every correction into a permanent fix. When a human catches something, that class of error should never reach a human again. If we are catching the same thing in month three that we caught in week one, the system is not learning.
• Delete the check-ins you created. Your scorecard is not how many systems need watching. It is how few.

The measure of success: more of the business covered, less human attention required. Those two numbers should move in opposite directions month over month, and you should be the one who can prove they did.

• You are already building on Open Claw or Hermes today
• You already build agents nobody asked you to build
• You have gone back and made something better after it already worked
• You get an actual kick out of deleting your own recurring tasks
• You want to own a hard problem end to end rather than manage people
• You are fine being handed an outcome and left to decide the how
• You want your work touching a real product with real customers and real money

• You have never used Open Claw or Hermes and do not want to
• You want a spec, a ticket queue, and a defined scope
• You want to be told which tools and which architecture to use
• Your best work is…