About the role
Shopify'sShop App Agentis the AI assistant buyers talk to inside the Shop app — product discovery and search, orders, shipping and tracking, account questions, and Shop Cash.
When a shopper is unhappy with one of its answers, they tap thumbs-down — and that's all they leave.
No comment, no reason, no survey.
Your job is to work out why, from the transcript alone.
What you'll do
Read a full shopper agent conversation, including the tool calls the agent made and what came back
Find the specific turn that received the thumbs-down
Classify it with one primary tag — the domain of the complaint: a disliked recommendation, a failed order lookup, an action the agent couldn't take, a rejection of AI itself, or a genuinely broken response — and one specific secondary tag within it
Write a short comment: what the shopper wanted, what the agent did or failed to do, and why those tags fit
Commitment:~5 hours/week
What makes someone valuable at this
The hardest part is not guessing.
Plenty of thumbs-downs have no visible cause — the agent did nothing wrong, or the shopper simply didn't like the answer.
There is an explicit unknown label for exactly that, and using it honestly matters more than producing a confident-sounding reason.
We would rather record "we don't know why" than invent a cause that sends the wrong signal to the model.
Qualifications
Required:




Rule discipline— you can apply a fixed taxonomy the same way across hundreds of conversations, and you notice when a case sits between two labels rather than forcing it.
Comfort reading structured traces— tool calls, their inputs, and their raw outputs, so you can tell what the agent actually did from what it merely claimed.
Restraint under ambiguity— you are willing to label something unknown rather than reach for a plausible-sounding cause.
Clear, concise written English— enough to explain in two or three sentences why a conversation got the labels you gave it.
Strongly preferred:
Consumer e-commerce fluency— orders, tracking, returns, refunds, and a working sense of where a merchant's responsibility ends and the platform's begins.
Prior annotation, labelling, or model-evaluation workagainst a defined rubric or taxonomy.
Preferred (nice to have — we'll ramp you on the specifics):
Hands-on experience with the Shop app or other AI shopping assistants as a shopper.
Familiarity with customer-support operations and escalation paths.
What this is not
This is not a customer-support role and not an engineering role.
You are not fixing the agent and not replying to shoppers — you are diagnosing why a real shopper was unhappy, precisely and repeatably, so the team can measure where the agent falls short.
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📌 E-Commerce Annotation Specialist (New South Wales)
🏢 Mercor
📍 New South Wales