The posting, in Mercor's own words
archived Oct 15, 2026About Mercor
Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.
The Role
Mercor produces all sorts of data of data types, for AI labs and enterprise customers. We're hiring someone to own pricing structures across our data types. This person will build the pricing models, rate cards, and margin frameworks to ensure pricing is consistent and defensible.
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What You'll Do
Acquisition pricing & cost modeling (buy side) Establish fair market value for the data we buy — by data type, domain, and end buyer — and set the acquisition cost ceilings that bound what we'll pay Track competitor bidding behavior and maintain the cost curves that tell us when to pay up to win a corpus and when to walk Model fully-loaded cost to serve: acquisition price, extraction, human QC, anonymization/PII, and LLM inference — not just what we pay the vendor Define the priceable unit for each data type — per repo, per file, per video minute, per audio hour — and the quality attributes that move price within a unit Monetization & margin strategy (sell side) Design pricing and packaging frameworks for data by data type, volume, format, and customer segment Build rate cards and tiered pricing for recurring, repeatable data products Own unit economics per deal and per data product, including transfer pricing, internal chargeback, and direct vs. indirect revenue attribution across account P&Ls Build the pricing/forecasting toolkit Finance and leadership use to evaluate new data products before launch Continuously test and refine pricing using win/loss data, renewal behavior, and market feedback