In this article, we will look at how the LinkedIn engineering team rebuilt the Feed and the challenges they faced.

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LinkedIn replaced five separate Feed retrieval systems with a single LLM-powered dual encoder model to serve 1.3 billion users. Key engineering decisions include: converting raw numerical features into percentile buckets (boosting popularity-embedding correlation 30x), filtering training data to only positively-engaged posts (2.6x faster training, 15% better recall), using both easy and hard negatives for contrastive learning, and building a Generative Recommender with causal transformer attention and Multi-gate Mixture-of-Experts heads for multi-task ranking. Infrastructure innovations include shared context batching, a custom Flash Attention variant (GRMIS) for 2x speedup, disaggregated CPU/GPU serving, and continuously running embedding refresh pipelines.

How LinkedIn Feed Uses LLMs to Serve 1.3 Billion Users

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