LinkedIn scales AI people search to 1.3 B users, lifts non‑degree hires 10%
When I first saw LinkedIn roll out its AI-driven recruiting tools, I was surprised to learn the platform now touches about 1.3 billion users worldwide. That’s a lot of people scrolling, connecting and, apparently, looking for jobs. Earlier this year the company introduced an AI Job Search feature.
Erran Berger, the VP of Product Engineering, says the tool nudged job seekers without a four-year degree roughly 10 percent closer to landing a role - a modest but tangible bump. Now LinkedIn is pushing a new people-search capability that promises quicker matches at scale. It feels like they’re trying to take what worked in that small test and stretch it across the whole network.
That raises a few questions: are the lessons from the pilot really ready for a global audience, or will the system stumble when it hits a wider pool? The firm claims the earlier win gave them a blueprint, and they’re using it to tackle a far bigger problem. “It’s …”
The success of its previously launched AI Job Search -- which led to job seekers without a four-year degree being 10% more likely to get hired, according to VP of Product Engineering Erran Berger -- provided the blueprint. Now, the company is applying that blueprint to a far larger challenge. "It's one thing to be able to do this across tens of millions of jobs," Berger told VentureBeat.
"It's another thing to do this across north of a billion members." For enterprise AI builders, LinkedIn's journey provides a technical playbook for what it actually takes to move from a successful pilot to a billion-user-scale product. The new challenge: a 1.3 billion-member graph The job search product created a robust recipe that the new people search product could build upon, Berger explained. The recipe started with with a "golden data set" of just a few hundred to a thousand real query-profile pairs, meticulously scored against a detailed 20- to 30-page "product policy" document.
To scale this for training, LinkedIn used this small golden set to prompt a large foundation model to generate a massive volume of synthetic training data.
LinkedIn just rolled out its AI-powered people search, and I can’t help wondering if it will actually deliver. The feature shows up three years after ChatGPT first hit the scene and roughly six months after LinkedIn’s own AI job-search experiment. That pilot, according to VP Erran Berger, nudged candidates without a four-year degree about 10 % closer to a hire.
Now the company is trying to copy that play for its 1.3 billion members - a scale most firms seem to avoid. Building generative AI at that size feels, as insiders put it, “slow, brutal,” so technical snags are probably still around. LinkedIn says the new search will sharpen matches, but beyond the earlier hiring lift they haven’t released hard numbers.
It’s also fuzzy whether the tool will really open doors for non-degree workers everywhere, especially outside the U.S. All told, the move feels bold; whether it works in practice will hinge on how LinkedIn tackles the scaling problems it’s already admitted it faces.
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Common Questions Answered
How did LinkedIn's earlier AI Job Search feature impact non‑degree job seekers?
The AI Job Search tool increased the likelihood of candidates without a four‑year degree landing a role by 10 percent, according to VP of Product Engineering Erran Berger. This improvement demonstrated the effectiveness of AI‑driven matching for a traditionally underserved group.
What is the scale of LinkedIn's new AI people‑search capability?
LinkedIn's AI people search now serves roughly 1.3 billion users worldwide, extending the technology from tens of millions of jobs to a user base exceeding a billion members. This massive rollout marks one of the largest generative AI deployments in the recruiting sector.
Which executive provided the blueprint for scaling LinkedIn's AI recruiting tools?
VP of Product Engineering Erran Berger outlined the blueprint, noting that the success of the AI Job Search feature informed the approach for the broader AI people‑search rollout. He emphasized that replicating the earlier results at a much larger scale required careful engineering and data handling.
What challenges does LinkedIn face in delivering AI‑driven matches at a billion‑user scale?
Scaling generative AI to over a billion members involves handling massive data volumes, ensuring real‑time relevance, and maintaining privacy across diverse markets. LinkedIn must also address potential biases and keep the matching algorithms accurate as the user base grows exponentially.