LinkedIn launched Qualified Applicant (QA) AI model, which learns the kinds of applicant skills and experience that a hirer is looking for based on their engagement with past candidates.
Active job seekers apply for many jobs and hear back from only a few. At the same time, hirers (recruiters, hiring managers, business owners looking to recruit a new employee, etc.) are flooded with applications with limited time to screen each, and as a result, they often overlook qualified candidates. This inefficiency frustrates both sides.
To address these problems, the platform built the Qualified Applicant (QA) AI model, which learns the kinds of applicant skills and experience that a hirer is looking for based on the hirer’s engagement with past candidates. It uses the model to help its members find jobs for which they have the best chance to hear back from and to reduce the likelihood of the hirers overlooking promising applicants by highlighting those who are a great fit.
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Several challenges exist in solving this problem. One is creating a model that is effective for all job seekers and hirers, done with model personalization. Another is to acknowledge that individual job searches and job postings are transient and that personalized models will go stale if not updated regularly. Another challenge is the scale at which personalized models must be trained. The QA model has billions of coefficients, so scalability in training is paramount.
Qualified Applicant is an AI model that aims to predict how likely a member is to hear back if he or she applies for a particular job. Formally, we try to predict the probability of a positive recruiter action, conditional on a given member applying to a given job.
What constitutes a positive recruiter action depends on the specific context. This can include viewing an applicant’s profile, messaging them, inviting them to an interview, or ultimately, sending them a job offer.