--- title: The Training Infrastructure Behind AI-Powered Job... description: LinkedIn's engineering team details how they rebuilt the training infrastructure for AI-Powered Job Search using multi-teacher knowledge distillation. The... canonical: https://daily.dev/posts/the-training-infrastructure-behind-ai-powered-job-search-8x-faster-multi-teacher-distillation-sfwknf7xt twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The Training Infrastructure Behind AI-Powered Job Search: 8X Faster Multi-Teacher Distillation | daily.dev og:description: LinkedIn's engineering team details how they rebuilt the training infrastructure for AI-Powered Job Search using multi-teacher knowledge distillation. The... og:url: https://daily.dev/posts/the-training-infrastructure-behind-ai-powered-job-search-8x-faster-multi-teacher-distillation-sfwknf7xt og:image: https://api.daily.dev/og/posts/SFWKNF7xt.png og:image:alt: The Training Infrastructure Behind AI-Powered Job Search: 8X Faster Multi-Teacher Distillation og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The Training Infrastructure Behind AI-Powered Job Search: 8X Faster Multi-Teacher Distillation **[LinkedIn Engineering](https://daily.dev/sources/linkedin)** · 16 min read · 0 upvotes · 0 comments ## Summary LinkedIn's engineering team details how they rebuilt the training infrastructure for AI-Powered Job Search using multi-teacher knowledge distillation. The system coordinates a relevance teacher, engagement teacher, and embedding model to train a compact 0.6B student SLM capable of serving ~22,000 requests/sec/GPU. Key optimizations include: migrating to FSDP2 with Hybrid Sharded Data Parallel (HSDP), switching to high-throughput inference engines with continuous batching and paged attention (2X faster teacher inference, 4X larger batches), and introducing offline distillation with per-shard caching on HDFS to decouple teacher inference from student training. The result is an 8X end-to-end speedup — from ~45 hours to under 5 hours — with no loss in model quality. The entire pipeline is unified under a Ray-based framework where online and offline distillation are per-teacher decisions within a single run, enabling same-day experimentation cycles. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is multi-teacher knowledge distillation and why would you use several teacher models instead of one? Multi-teacher distillation trains a single compact student model to learn from several specialized teacher models simultaneously, each providing a different signal type. In LinkedIn's job search ranking system, a relevance teacher provides soft labels over yes/no tokens, an engagement teacher provides multi-task soft targets for member actions, and an embedding model contributes dense representations, all fused into one 0.6B student model for low-latency serving. _daily.dev surfaces engineering writeups like this for teams weighing single- versus multi-teacher distillation setups._ ### How can you speed up knowledge distillation training when teacher inference becomes the bottleneck? Decoupling teacher inference from student training with an offline caching pipeline can cut training time dramatically. Each teacher runs inference once, writes soft labels or embeddings to storage keyed by model version and data fingerprint, and the student then trains purely by reading cached outputs with no teacher GPUs needed, taking LinkedIn's pipeline from roughly 45 hours down to under 5 hours end-to-end. _Engineers optimizing training pipelines track infrastructure techniques like this via daily.dev._ ### What throughput can a distilled small language model achieve in production serving compared to its teacher models? A compact 0.6B parameter student model distilled from multi-billion-parameter teachers can serve roughly 22,000 requests per second per GPU while reaching near-teacher ranking accuracy. This trade shifts computational cost from serving to training, since the large teachers only need to run during the training phase rather than at inference time. _daily.dev helps ML practitioners follow real production numbers on distillation trade-offs like this one._ ## Similar posts on daily.dev - [How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation](https://daily.dev/posts/how-linkedin-trains-ai-job-search-8x-faster-with-multi-teacher-distillation-ero71wxem) · InfoQ · 1 upvotes · 0 comments - [Reimagining LinkedIn’s search tech stack](https://daily.dev/posts/reimagining-linkedin-s-search-tech-stack-mwajgbtsf) · LinkedIn Engineering · 1 upvotes · 0 comments - [Job Searcher](https://daily.dev/posts/job-searcher-jnnq5luqj) · Hugging Face · 19 upvotes · 1 comments - [Build Efficient Financial Data Workflows with AI Model Distillation](https://daily.dev/posts/build-efficient-financial-data-workflows-with-ai-model-distillation-denmyunvq) · NVIDIA Developer · 0 upvotes · 0 comments - [How Big Models Teach Small Models to Be Smart](https://daily.dev/posts/how-big-models-teach-small-models-to-be-smart-3wi68af3j) · ByteByteGo · 8 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#pytorch](https://daily.dev/tags/pytorch) [View this post on daily.dev](https://daily.dev/posts/the-training-infrastructure-behind-ai-powered-job-search-8x-faster-multi-teacher-distillation-sfwknf7xt) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"The Training Infrastructure Behind AI-Powered Job Search: 8X Faster Multi-Teacher Distillation","url":"https://daily.dev/posts/the-training-infrastructure-behind-ai-powered-job-search-8x-faster-multi-teacher-distillation-sfwknf7xt","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-training-infrastructure-behind-ai-powered-job-search-8x-faster-multi-teacher-distillation-sfwknf7xt"},"datePublished":"2026-08-06T13:14:31.950Z","dateModified":"2026-10-15T02:22:52.761Z","description":"LinkedIn's engineering team details how they rebuilt the training infrastructure for AI-Powered Job Search using multi-teacher knowledge distillation. The...","image":"https://media.daily.dev/image/upload/s--0_ODbtD2--/f_auto/v1722860399/public/Placeholder%2008","thumbnailUrl":"https://media.daily.dev/image/upload/s--0_ODbtD2--/f_auto/v1722860399/public/Placeholder%2008","isAccessibleForFree":true,"articleSection":"LinkedIn Engineering","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"LinkedIn Engineering","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/le","url":"https://daily.dev/sources/linkedin"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/the-training-infrastructure-behind-ai-powered-job-search-8x-faster-multi-teacher-distillation-sfwknf7xt","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,pytorch","timeRequired":"PT16M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"LinkedIn Engineering","item":"https://daily.dev/sources/linkedin"},{"@type":"ListItem","position":3,"name":"The Training Infrastructure Behind AI-Powered Job Search: 8X Faster Multi-Teacher Distillation"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/the-training-infrastructure-behind-ai-powered-job-search-8x-faster-multi-teacher-distillation-sfwknf7xt#faq","mainEntity":[{"@type":"Question","name":"What is multi-teacher knowledge distillation and why would you use several teacher models instead of one?","acceptedAnswer":{"@type":"Answer","text":"Multi-teacher distillation trains a single compact student model to learn from several specialized teacher models simultaneously, each providing a different signal type. In LinkedIn's job search ranking system, a relevance teacher provides soft labels over yes/no tokens, an engagement teacher provides multi-task soft targets for member actions, and an embedding model contributes dense representations, all fused into one 0.6B student model for low-latency serving. daily.dev surfaces engineering writeups like this for teams weighing single- versus multi-teacher distillation setups."}},{"@type":"Question","name":"How can you speed up knowledge distillation training when teacher inference becomes the bottleneck?","acceptedAnswer":{"@type":"Answer","text":"Decoupling teacher inference from student training with an offline caching pipeline can cut training time dramatically. Each teacher runs inference once, writes soft labels or embeddings to storage keyed by model version and data fingerprint, and the student then trains purely by reading cached outputs with no teacher GPUs needed, taking LinkedIn's pipeline from roughly 45 hours down to under 5 hours end-to-end. Engineers optimizing training pipelines track infrastructure techniques like this via daily.dev."}},{"@type":"Question","name":"What throughput can a distilled small language model achieve in production serving compared to its teacher models?","acceptedAnswer":{"@type":"Answer","text":"A compact 0.6B parameter student model distilled from multi-billion-parameter teachers can serve roughly 22,000 requests per second per GPU while reaching near-teacher ranking accuracy. This trade shifts computational cost from serving to training, since the large teachers only need to run during the training phase rather than at inference time. daily.dev helps ML practitioners follow real production numbers on distillation trade-offs like this one."}}]} ```