Expert talent and human data for frontier AI labs and enterprises
Mercor is a talent marketplace and human-data platform for AI labs training and evaluating frontier models.
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Mercor runs a two-sided marketplace connecting AI labs with the domain experts they need to train and evaluate models. Professionals create a profile, complete an adaptive AI interview that adjusts its questions to their field, and receive offers through the work portal in three steps. Contract roles span medicine, law, consulting, software engineering, and dozens of other domains, mostly paying $50 to $250 per hour with an average contracted rate of $109 per hour and weekly payouts; interview responses are never sold or used to train customer models.
The platform's benchmarking arm publishes APEX, the AI Productivity Index, a family of evaluations that scores frontier models on real professional work: APEX v1 covers investment banking, management consulting, big law, and primary care tasks; APEX-Agents tests long-horizon, cross-application agent work; and APEX-SWE, built with Cognition, measures software engineering performance. Buyers can also license off-the-shelf, peer-reviewed datasets, including 2,620 APEX Agents tasks across eight professional domains, 12,000 SWEBench Ext. coding tasks, and 3,558 BrowseComp web-browsing tasks, with sample tasks delivered the same day.
Mercor serves frontier AI labs sourcing RLHF and evaluation data, enterprises deploying agents, and professionals seeking flexible remote AI work. All buyer engagements are quote-based through the sales team; there is no self-serve pricing.
For enterprises, Mercor offers four services: Agent Diagnostics to find high-value automation opportunities, Agent Deployment to build and integrate expert-informed agents into company workflows, Agent Optimization to improve agents already in production, and Data Monetization, which anonymizes workflow data using deterministic pattern matching and generative AI to mask more than 60 types of PII, PHI, and BII from 100-plus supported tools before licensing it to AI labs. The platform runs in the browser, and APEX datasets and evaluation code are published on Hugging Face and GitHub.
The AI Productivity Index, a benchmark family that scores frontier models on economically valuable tasks across investment banking, management consulting, big law, and primary care medicine.
Leaderboard measuring whether frontier AI agents can execute long-horizon, cross-application tasks across professional services jobs.
Software engineering benchmark built with Cognition that evaluates models on real-world work, from bug fixes to feature development.
Expert-built eval suites for enterprise agents, run as live production rubrics on real traffic or as offline APEX benchmarks, delivered as a reusable suite in under six weeks.
Adaptive AI-led interview that adjusts its questions to a candidate's field to evaluate expertise at scale before matching them to roles; responses are never sold or used to train customer models.
Builds agents infused with human expertise and integrates them into company workflows with deployment guardrails and observability.
Analyzes enterprise workflows and organizational knowledge to identify high-value agent deployment opportunities.
Lets enterprises license anonymized operational data to AI labs, with compensation based on data volume and depth, paid by wire transfer within 2-4 weeks.
Pre-built, peer-reviewed licensable datasets, including 2,620 APEX Agents tasks across eight domains, 12,000 SWEBench Ext. coding tasks, and 3,558 BrowseComp web-browsing tasks, with sample tasks delivered same day.
Browse and apply to remote AI contract roles, from internal medicine and cardiology experts at $130-$180/hr to machine learning engineers at $70-$250/hr, and receive offers in three steps.
Contractors are paid weekly for completed work, with more than $4M in daily payouts across the expert network.
Combines deterministic pattern matching and generative AI to irreversibly mask PII, PHI, and BII, detecting and neutralizing more than 60 types of sensitive identifiers across data from 100+ supported tools.
Pricing requires contacting the vendor.
Mercor is the rare three-year-old vendor you can defend to the board.
“Mercor matches vetted domain experts to frontier labs for model training and evaluation, and it publishes the APEX benchmark family. It's profitable, valued at $10 billion, and positioned as the neutral vendor the labs now want.”
Six of the Magnificent Seven already buy from Mercor. That answers my reference-check question before I've placed a call. A $350 million Series C at a $10 billion valuation in October 2025 answers the runway question.
APEX is the strategic tell. Publishing how frontier models score on real banking and law work makes Mercor the referee, and referees get invited to every game. Scale AI's Meta entanglement pushed neutral-vendor demand straight to them.
The catch: revenue reportedly concentrates in a handful of labs, contractor wage cuts drew press in late 2025, and a March 2026 supply-chain attack through LiteLLM exposed sensitive data for a limited subset of experts. Still, they turned a profit in the first half of 2025 while quintupling their valuation. Pilot a scoped evaluation project now, and negotiate terms while they're still land-grabbing.
Neutral standing after Scale AI's Meta deal plus the APEX referee role is a strong hand.
Reported late-2025 contractor wage cuts, a March 2026 supply-chain breach affecting some expert data, and lab revenue concentration are the visible flags.
Same-day dataset samples and an existing 5M+ expert network shorten the first engagement.
Expert data and evaluation are the exact inputs frontier AI programs are short on.
A $350M Series C at a $10B valuation plus a reported H1 2025 profit is unusual durability for a 2023 founding.
AI teams who need vetted domain experts for large evaluation programs.
Small teams who need self-serve pricing without a sales cycle.
The strongest supply-side bet for teams that need thousands of calibrated expert evaluators.
“Mercor treats expert evaluation as a recruiting-and-calibration problem and solves it with adaptive screening across a network of more than five million experts. For long-horizon evaluation programs, the supply pipeline matters more than any single dataset, and this one is the deepest visible.”
Expert supply, not task design, is the bottleneck in frontier evaluation programs now. Mercor's AI Interview screens candidates adaptively by field, which is the only credible way to keep a network of five million experts current. If the screening holds calibration, the refresh problem gets solved at the source.
Coverage runs from internal medicine and cardiology specialists at $130 to $180 an hour through consulting, law, and software engineering, with weekly payouts keeping the pool warm. APEX-Agents — 2,620 peer-reviewed tasks across eight professional domains — proves they can turn that pool into durable evaluation assets, not just staffing.
However, quote-based buying means every new evaluation workstream routes through their sales team, and Surge AI competes for the same lab budgets with deeper RLHF roots. If you're standing up a three-year evaluator program, this is the strongest supply-side foundation visible in the category.
Publishing APEX-Agents and open datasets makes it the visible standard-setter against Surge AI and Turing.
Purpose-built for sourcing calibrated expert evaluators, which is precisely the scarce input.
100+ supported tools on the data-partnership side; buyer-side work runs through quote-based services.
The expert network holds value across cycles, but lab budget concentration shapes the roadmap.
The AI Interview plus the APEX benchmarking arm compound into a supply moat, not a staffing agency.
Data operations leaders who run expert evaluation programs at scale.
Teams who only need occasional crowdsourced annotation.
Expert labor runs $50 to $250 an hour; everything above the rate is a negotiation.
“All buyer engagements are quote-based with no published platform pricing. Posted expert rates and same-day dataset samples give partial cost visibility, but the total bill is a sales conversation.”
The visible number is labor: $50 to $250 an hour, averaging $109 contracted. Everything above that rate is negotiated. No rate card, no published take rate, no tiers.
Off-the-shelf improves the math. Licensing the 12,000-task SWEBench Ext. set or 3,558 BrowseComp tasks beats commissioning custom work, and sample tasks arrive same day. Data Monetization runs the other direction — it pays enterprises by wire within 2 to 4 weeks, turning workflow exhaust into a revenue line.
Turing sells through the same quote-based motion, so no penalty for the category norm. The catch is year-three cost can't be modeled without a negotiation, and a vendor that grew from $100 million to $500 million run rate inside six months has pricing power. Budget a buffer.
Weekly contractor payouts and 2-4 week wire settlements show functioning money operations.
Quote-based, sales-led engagements with no visible self-serve or standard terms.
Expert hourly rates are posted; platform pricing and take rate are not.
APEX scoring and same-day samples make deliverable value unusually measurable for this category.
Off-the-shelf datasets and posted labor rates anchor cost, but custom work is unbounded upward.
Procurement teams who can budget for negotiated enterprise data contracts.
Buyers who require published pricing before a sales call.
Adaptive screening plus weekly payouts fix the two worst jobs in expert sourcing.
“The AI Interview compresses specialist screening from weeks of calls into ranked candidates per field. Weekly payouts and posted rate bands keep a large expert pool from churning.”
Screening a thousand specialist applicants normally eats a week of calls. The AI Interview runs that screen adaptively per field and hands you ranked candidates, which is the difference between sourcing ten evaluators and sourcing five hundred.
Offer flow is three steps, which keeps specialists from ghosting mid-funnel. Weekly payouts — more than $4 million moving daily across the network — remove the reconciliation mess that burns out expert pools on Scale's Outlier. Posted rate bands, like machine-learning engineers at $70 to $250 an hour, are rare candor in this category.
The friction sits at the edges, however. New project types route through Mercor's services team rather than self-serve tooling, and public documentation on rubric calibration is thin. For sourcing and paying specialists at volume, it still beats stitching a job board to a spreadsheet.
Adaptive AI Interview screening plus a three-step offer flow removes the worst manual sourcing work.
APEX methodology is published openly, but buyer-facing rubric-calibration docs are thin.
New project types route through the services team instead of self-serve tooling.
Rate bands, domain coverage, and a 5M+ expert network reward sourcing at serious volume.
Weekly payouts at $4M-a-day scale fold payout operations into the platform.
Talent operations leads who source specialist contractors at volume.
Teams who want hands-on control of every screening interview.
A talent platform that pays weekly and answers in three steps earns real goodwill.
“The contractor experience is unusually respectful, with three-step offers, posted rates, and weekly pay. The AI-led screening is efficient but leaves you without a human when it matters.”
Three steps from profile to offer is the whole pitch, and it's a good one. Most talent platforms make you re-enter your resume four times and then go quiet for a month. The Work Portal at least respects the clock.
Getting paid weekly instead of net-60 is the detail that tells you someone there has actually freelanced. The average contracted rate is $109 an hour, with posted ranges up to $250, so nobody's reverse-engineering their worth from a black box.
Everything runs in the browser, no native app, so checking offers from your phone is a mobile-web experience. The catch with an AI-led interview: if it misreads your specialty, there's no human on the line to appeal to. Still, next to Upwork's bid-and-pray churn, this feels like a staffing agency that answers its email.
Posted rates and a clean three-step offer flow beat category norms for respect.
The marketplace mechanics are familiar to anyone who has freelanced before.
Browser-only platform; neutral score since the work itself is desktop-shaped.
Profile, adaptive interview, offer — three steps with no resume re-entry loop.
Over $4M in daily payouts landing weekly suggests the money side just works.
Professionals who want flexible remote AI work with fast pay.
Candidates who want a human recruiter through the hiring process.
Fast, profitable, and dependent on a handful of labs staying hungry for expert data.
“The growth and profitability look real in all visible reporting. So does the customer concentration, and the November 2025 wage cuts show how fast the economics can shift.”
Companies founded in 2023 don't normally report a $2 billion run rate, as Mercor's CEO did in July 2026. When one does, I ask who's paying. The answer is a handful of frontier labs, with revenue reportedly concentrated among a few of them.
Credit where due. APEX-SWE, the software benchmark built with Cognition, publishes an open subset on Hugging Face, contractor rates average a stated $109 an hour, and they report an actual first-half 2025 profit. That's more disclosure than Toloka manages.
But watch three things. In November 2025, Business Insider reported a Meta project moving contractors from $21 to $16 an hour, a cut of about a quarter, and curated expert pools remember that. Datasets port cleanly on exit; custom pipelines don't. And if lab data budgets tighten, concentrated revenue tightens with them. Solid company, fragile altitude.
Post-Meta neutrality and the APEX referee position genuinely separate it from Scale AI.
Licensed datasets survive an exit; custom pipelines and network access don't.
Profitable and well-capitalized, but the revenue base is a handful of lab budgets.
'Organizing human intelligence' is grand, but rates and benchmark methodology are published openly.
The CEO-reported $2B run rate, stated profitability, and lab customers are corroborated by independent reporting.
Labs who need expert data now from a well-capitalized vendor.
Buyers who want a vendor with a decade of history.
Common questions answered by our AI research team
Listed contract roles mostly pay $50 to $250 per hour, with an average contracted rate of $109 per hour. Rates depend on expertise, project complexity, and demand, and contractors are paid weekly, with over $4M in daily payouts across the network.
APEX is Mercor's AI Productivity Index, a benchmark family scoring frontier models on real professional work. APEX v1 covers banking, consulting, law, and medicine; APEX-Agents tests long-horizon agent tasks; APEX-SWE, built with Cognition, measures software engineering; and APEX-Accounting, built with Ramp, tests professional accounting work.
Create a profile, complete an adaptive AI interview that tailors questions to your field, and receive offers through the work portal. Roles are fully remote contracts lasting weeks to months, and Mercor says you can get your first offer in three steps.
Yes. Its pipeline combines deterministic pattern matching with generative AI to irreversibly mask PII, PHI, and BII, detecting and neutralizing more than 60 types of sensitive identifiers before workflow data reaches AI labs.
Mercor says it supports 100+ tools, including Slack, Microsoft Teams, Gmail, Outlook, Google Drive, Confluence, Notion, Salesforce, and GitHub. Connecting more tools generally raises the payout, and no data leaves your systems until you approve it.
Who Mercor competes with, and how each one differs
Scale AI runs the largest AI-data-labeling pipeline and, like Mercor, now recruits skilled contractors for model training and evaluation; the two clashed in a September 2025 trade-secrets lawsuit after Scale accused a former employee of taking customer files to Mercor, and several AI labs later shifted work away from Scale following Meta's 2024 investment and hire-away of its CEO.
Surge AI is a similarly-sized ($20B+ valued) expert-contractor and RLHF data platform serving frontier AI labs, named alongside Scale AI as Mercor's closest rival in press coverage; unlike Mercor it has stayed almost entirely private, disclosing few details about its operations, funding or workforce.
Labelbox is a data-labeling and RLHF platform (via its Alignerr expert-annotator network) serving AI labs, positioned more as annotation tooling plus a labeling workforce than Mercor's talent-marketplace model, which recruits and directly bills out named domain experts - doctors, lawyers, ex-bankers - by the hour.
Turing began as a remote software-developer staffing marketplace and later added an AI-training/evaluation line (Turing AGI Advancement) that competes with Mercor for AI-lab contracts, though its core business remains engineering placement rather than cross-industry expert data work.
