LinkedIn Moves Into AI Training With New "Labor Marketplace"
LinkedIn is testing an AI labor marketplace paying verified specialists $150/hr, marking Big Tech's entry into a space dominated by Mercor and Scale AI.
LinkedIn is in the early stages of launching an "AI labor marketplace" where members can earn up to $150 an hour training AI chatbots on everything from coding to nursing to finance, Business Insider first reported on April 14, 2026. The Microsoft-owned platform confirmed the early-stage testing and has posted over a dozen public listings for AI trainers across coding, healthcare, finance, linguistics, and adversarial security testing.
The move puts LinkedIn in direct competition with a cohort of fast-growing AI training startups that have collectively reached tens of billions in valuation — and that have also accumulated a mounting list of data-security problems.
The Rollout
LinkedIn is building an on-demand marketplace for verified members to complete specialized AI training tasks. According to LinkedIn's official FAQ, the program is "slowly rolling out" and reflects the company's "commitment to creating economic opportunities for every member of the global workforce." Availability is limited during early testing and will expand as LinkedIn collects member feedback.
Once on the platform, approved trainers rate AI model outputs, identify flaws, test system limits, and provide domain expertise — all tasks that help AI labs improve chatbots and language models. LinkedIn has also rolled out a feature that sends members notifications whenever a new training opportunity matching their expertise appears.
The Assessment Pipeline
Eligibility requires three steps, per LinkedIn's official documentation. First, members must verify their identity with a government-issued ID. Second, they must complete an AI-powered conversation — built on Microsoft's Azure OpenAI API — in which an AI asks questions about their professional background to assess fit for specific projects and suggest profile updates. The conversation must include at least three answered questions to count as complete. Third, applicants complete project-specific tasks that vary by role.
LinkedIn also administers a structured annotation skills assessment covering five core task types: Evaluation, Preference Ranking, Rubric Work, Supervised Fine Tuning (SFT), and Agentic Work. Scores are calculated per question and per skill type on a defined scale (for example, 1–5), and results are shared with the hiring teams behind specific AI training projects. Integrity signals — such as large copy-paste events — are also captured to flag potential misuse.
For domain-specific roles in law, medicine, or finance, LinkedIn additionally draws on profile data such as education, licenses, and work experience to match subject-matter experts to the right tasks.
Compensation Benchmarks
| Role | Hourly Rate |
|---|---|
| Senior software engineer (code review & testing) | Up to $150/hour |
| Medical and nursing experts (clinical reasoning evaluation) | Up to $100–$120/hour |
| Finance experts (Excel & financial reasoning) | Up to $100/hour |
| Linguists (Germanic & Nordic languages) | Up to $100/hour |
| Red-team / security testers (adversarial testing) | $40–$50/hour |
LinkedIn notes that actual pay will vary by client and project.
The Trust Strategy
This move puts LinkedIn in direct competition with a cohort of rapidly growing AI training startups. Mercor quintupled its valuation to $10 billion in less than a year, while Surge AI — which owns the human-expert marketplace Data Annotation — reached $24 billion, according to Forbes. But those companies have also accumulated serious reputational damage. Scale AI left confidential contractor and client information exposed across hundreds of Google Docs before locking them down after Business Insider revealed the practice. Mercor was hit by a major data breach in early 2026 that triggered five class-action lawsuits in a single week.
LinkedIn is explicitly capitalizing on this trust gap. By requiring government-ID verification and leveraging its existing professional network, the platform is positioning itself as a more credible, accountable alternative for AI labs like OpenAI and Anthropic. It's also professionalizing work that was previously anonymous and fragmented — turning what has been called "ghost work" into visible roles people can add to their career profiles and build credentials around.
The Automation Risk
The "replacement" problem is real. Communities like r/recruitinghell are already warning that experts who take high-paying training roles are essentially "selling their own seeds" — directly contributing to systems that could eventually automate their specific high-value functions. A nurse helping an AI master clinical reasoning, or a senior engineer reviewing code patterns, is training models that could displace workers in those exact roles.
Broader hiring trends reinforce the concern. LinkedIn's own April 2026 Labor Update shows entry-level hiring in "AI-augmented" roles — such as Junior Developer positions — fell 8.9% while AI training gigs grew. Companies appear to be substituting expert-driven AI training for entry-level hiring, which could hollow out the pipeline through which workers develop real-world skills.
Data security remains an open question even for LinkedIn. The sector's recent track record suggests that handling biometric verification footage and sensitive professional data at scale is not a solved problem. LinkedIn's identity verification process means the company will hold extensive personal data from trainers — and what happens to that data if there's a breach, who can access it, and how long it's retained are questions LinkedIn's current documentation does not fully answer.
Forward Outlook
The real test is whether LinkedIn opens this marketplace broadly or keeps it selective. A public launch would likely accelerate direct partnerships between LinkedIn and major AI labs, reshaping the market structure that startups like Mercor and Scale AI currently dominate. Startups will face pressure on pricing and market share as LinkedIn leverages its scale, network effects, and Microsoft's cloud infrastructure.
Watch for three signals: whether AI labs begin routing training work through LinkedIn rather than standalone vendors; whether entry-level tech hiring continues to fall as training gigs expand; and whether LinkedIn's compliance and data-security posture holds up to the scrutiny that has destabilized its competitors.
LinkedIn's move signals Big Tech's intent to control not just the deployment of AI but the human-powered pipeline that trains it. For workers, that means well-paid, credentialed opportunities with a platform they already use. For the labor market, it raises harder questions about automation of expertise, concentration of influence over AI development, and whether the economy being built around training AI is sustainable once those same systems become capable enough to reduce demand for the trainers who built them.
Sources
Related reading
Mercor review — the leading independent marketplace LinkedIn is now competing with.
Best AI training platforms compared — how the field looks as LinkedIn enters it.
How to find AI training jobs — strategies for navigating a market that now includes LinkedIn.
Mercor profile optimization — how to position your profile on existing platforms as competition grows.
Pietro R.
MSc Human-Computer Interaction | Founder & Product Owner
Pietro is the founder and technical lead of aitrainer.work. He builds and maintains the platform's data pipeline, certification infrastructure, and editorial standards.