Inside LinkedIn's L1/L2 rankers: twin MLP models scoring topic fit and dwell time

_jaydeepkarale · x · 2026-11-06

LinkedIn's multi-stage ranking uses L1 and L2 rankers with nearly identical architectures (text embedding models + MLP), differing only in size and passed parameters. Each stage outputs on-topicness and long-dwell scores, combined by a multi-objective score combiner to decide which posts are finally shown to users.

Related event: Inside LinkedIn's Semantic Search Overhaul: Two-Tower Retrieval and Two-Stage Ranking(6 posts)→

Original post →

More from Research

Research channel →