🔍LinkedIn's AI Job Search Training Infrastructure Details Revealed
LinkedIn's secret sauce for faster, smarter job searches
TL;DR
LinkedIn unveiled its AI job search training system, using a multi-teacher distillation pipeline to compress large models into a 0.6B-parameter model, boosting job search results by 24.48% NDCG@10. It's live for US users, offering insights for teams aiming for real-time latency and cross-encoder-quality ranking.
LinkedIn just dropped the curtain on its AI-powered job search training infrastructure, which uses a multi-teacher distillation pipeline to compress knowledge from large models into a compact 0.6B-parameter ranking model. This system not only improves job search results by 24.48% NDCG@10 but also slashes training time by 8x through a mix of optimizations like LiGer, multi-node training, and FSDP2. If you're building similar systems, this is a must-read. The infrastructure can handle hundreds of thousands of queries per second, and it's live for US users, offering a blueprint for teams aiming for real-time latency and cross-encoder-quality ranking.
Key Points
LinkedIn's system uses a multi-teacher distillation pipeline to compress large models into a 0.6B-parameter ranking model.
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