About the role

Shopify's Shop App Agent is 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 positive 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 work against 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 (City of Sydney)
🏢 Mercor
📍 City of Sydney