LinkedIn: A Transformer That Ranks Your Feed

A deep dive into how LinkedIn replaced its DCNv2 feed ranker with Feed SR — a decoder-only transformer that reads up to 1,000 of a member's past impressions as an ordered sequence, served on a disaggregated CPU/GPU stack with custom kernels. We cover the business context (a 1.2B-member feed whose legacy ranker couldn't model history as a sequence), the technical approach (RoPE positional embeddings, a parallel-DCNv2 scoring head, shared-context batching, and a custom flash-attention CUDA kernel), the honest negative results (why a fine-tuned LLM-ranker and a TransAct-style encoder were tried and rejected), and the realized impact: +2.10% time spent and +3.52% social actions in a production A/B test — at roughly 0.7× the inference energy of the CPU-served incumbent despite being a larger model. Source article: "An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking (Feed SR)" — LinkedIn / arXiv, https://arxiv.org/abs/2602.12354 (published 2026-02).