recruiting · saas · workflow
LinkedIn extracts skills from unstructured content using AI to power the LinkedIn Skills Graph
Skills across LinkedIn's platform are embedded in unstructured content — member profiles, job postings, and learning courses — and not consistently listed in structured fields, making comprehensive skills-based matching difficult to build at scale.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Content sources trigger extraction
Skill extraction is triggered by content across LinkedIn including job postings, member profiles, resumes, LinkedIn Learning courses, and feed posts.
Tools used
Multilingual BERTLLMTransformerKnowledge DistillationSparkSamza-BEAM
Outcome
LinkedIn's multitask AI skill extraction system improved job recommendation, job search, and skills matching results across multiple A/B tests — with gains in predicted confirmed hires, qualified applications, and PPC revenue. Knowledge Distillation enabled 80% model size reduction for nearline serving while meeting strict latency requirements.
Results
Time savedunder 100 milliseconds
Volume80%
Cost replaced+0.7577%
Grounding & classification
Source type: technical build writeup
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data extractionenterprise searchpersonalizationrecommendation systemknowledge baseresumemetric backedproduction runtime claimedsource backedtools describedworkflow describedsoftwareaccuracy improvementconversion increaserevenue increasethroughput increasetechnical build writeupdata entry opshr opsrecruitingdata sync enrichmentextract classify route