Retail Specialist

$60–80/hrrate as listed

Retail merchandising, category management, and operations experts write hard domain tasks and score LLM outputs against structured rubrics for a frontier AI lab. The engagement is W-2 through Cincinnatus LLC at roughly 35 hours/week on weekdays, with observed rates of $60–80/hr.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

What the work involves

You sit between a research team and the realities of running a retail business. Day to day, that means authoring retail problems the model is likely to get wrong — assortment tradeoffs, markdown cadence, planogram and space productivity decisions, vendor funding and trade terms, allocation and replenishment logic, shrink and inventory accuracy, promo lift forecasting — and then writing the reference answer with reasoning that a director-level peer would sign off on. The other half is evaluation: reading model responses, scoring them against a rubric, and writing feedback that explains precisely where the reasoning broke, not just that it did.

Expect to also work upstream on the rubrics themselves. Retail judgment is rarely binary — two reasonable merchants can disagree on a buy — so a large part of the job is defining what "correct" means, where partial credit belongs, and how calibration should hold across multiple SMEs working the same task family.

What the platform screens for

Depth that survives follow-up. Mercor's screen probes specifics: which categories you owned, what your open-to-buy looked like, what the actual decision was and what it cost.
A real evaluation track record. Prior hands-on rubric-based scoring of LLM or AI outputs is stated as mandatory — describe it concretely, including the rubric structure and volume.
Career progression at a recognized retailer (Amazon, Walmart, Target, Costco, Home Depot, Nike, or equivalent), with 8+ years dedicated to retail.
Written clarity. Feedback that a research engineer with no retail background can act on.

Logistics

Remote and W-2 through Cincinnatus LLC as employer of record, with placement inside a leading AI lab's extended workforce. The listing asks for reliable engagement of at least 35 hours/week during weekdays — this is closer to a full-time schedule than typical async gig work, though the hours themselves are largely self-managed around syncs with research teams. Pay is observed in the $60–80/hr range and is not guaranteed; final rates depend on the screen, specialization, and lab placement.

What they look for

8+ years in retail merchandising, category management, buying/planning, or retail operations at a top-tier retailer
Hands-on experience evaluating LLM or AI outputs against rubrics or structured scoring criteria
Demonstrable career progression into senior or director-level retail roles
Clear written reasoning and feedback for non-domain technical audiences
Rubric and evaluation-guideline design
Availability of 35+ hours/week on weekdays

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Do you have 8+ years of professional experience in retail functions such as merchandising, category management, retail operations, buying, or planning at a top-tier retailer (e.g., Amazon, Walmart, Target, Nike, Costco, Home Depot, or equivalent)?
Retail domain experience · required
Do you have prior hands-on experience evaluating LLM or AI model outputs against rubrics or structured scoring criteria?
AI model evaluation experience · required
Are you able to commit to at least 35 hours per week during weekdays?
Weekly availability · required

0 of 3 answered

Included with this listing

Interview prep — 11 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: Whether the candidate has genuine hands-on retail judgment—not just category knowledge, but the ability to weigh trade-offs (margin vs. velocity, shelf space vs. assortment) under real constraints and measure results.
A strong answer: The answer names a concrete business problem (e.g., declining attach rate in a category, inventory overstock, planogram inefficiency), describes the specific insight or analysis that drove the decision, shows awareness of constraints (labor, space, vendor relationships), and quantifies or clearly explains the business impact.
Where people lose it: Candidates often describe a project they oversaw or a recommendation they made, not a decision *they* owned. Or they cite generic best practices ('we optimized SKU count') without grounding it in actual retail conditions, constraints, or results.