Editorial illustration for LinkedIn credits multi-teacher distillation and small models for new LLM
LinkedIn's AI Job Search Gets Smarter with LLM Distillation
LinkedIn credits multi-teacher distillation and small models for new LLM
In the relentless race to build bigger AI models, LinkedIn quietly bet against the trend. The professional network needed an LLM that could interpret job queries, candidate profiles, and descriptions in real time, with surgical precision that mirrored its own product policies. What emerged wasn’t another gargantuan model.
Instead, the team engineered a breakthrough through multi-teacher distillation, distilling knowledge from a 7-billion-parameter teacher trained on a synthetic goldmine. Small models, they discovered, could deliver the speed and accuracy that prompting alone never could.
Why multi-teacher distillation was a 'breakthrough' for LinkedIn Berger and his team set out to build an LLM that could interpret individual job queries, candidate profiles and job descriptions in real time, and in a way that mirrored LinkedIn's product policy as accurately as possible. Working with the company's product management team, engineers eventually built out a 20-to-30-page document scoring job description and profile pairs "across many dimensions." "We did many, many iterations on this," Berger says. That product policy document was then paired with a "golden dataset" comprising thousands of pairs of queries and profiles; the team fed this into ChatGPT during data generation and experimentation, prompting the model over time to learn scoring pairs and eventually generate a much larger synthetic data set to train a 7-billion-parameter teacher model.
And that is the quiet revolution unfolding at LinkedIn. Not the biggest model wins, but the most teachable one. By forcing their LLM to internalize a 30-page policy document through multi-teacher distillation, they proved that precision doesn’t require scale.
A 7-billion-parameter teacher, a golden dataset, and countless iterations, this is engineering as editing, not as brute force. Small models, rigorously trained, can mirror complex product logic without the cost or unpredictability of a giant. The lesson for the industry is simple: stop chasing size.
Start chasing fidelity. LinkedIn’s breakthrough wasn’t a bigger model; it was a better teacher. And that changes everything.
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Common Questions Answered
What is multi-teacher distillation and how did LinkedIn use it for their LLM?
Multi-teacher distillation is a technique where knowledge from multiple larger models is transferred to a smaller, more efficient model. [arxiv.org](https://www.arxiv.org/pdf/2510.22101) describes this as a method to compress large neural networks by transferring knowledge from teacher models to a student model. For LinkedIn, this approach allowed them to create a lean LLM that could interpret job queries, candidate profiles, and job descriptions with high accuracy and low latency.
Why did LinkedIn move away from prompt engineering for their LLM?
LinkedIn found that prompt engineering led to unpredictable responses and increased latency when trying to handle complex interactions between job postings, résumés, and recruiter nuances. [ai21.com](https://www.ai21.com/glossary/foundational-llm/knowledge-distillation/) notes that model compression techniques like knowledge distillation can help create more efficient systems. By pivoting to a multi-teacher distillation approach, LinkedIn could develop a more precise and performant model that could parse multiple data types in a single pass.
How did LinkedIn ensure their LLM maintains product policy alignment?
The engineering team worked closely with product management to develop a comprehensive 20-to-30-page document that scored job description and profile pairs across multiple dimensions. [developers.google.com](https://developers.google.com/machine-learning/crash-course/llm/tuning) explains that fine-tuning allows adapting a foundation LLM to specific tasks by training it on task-specific data. This approach helped LinkedIn create an LLM that could accurately interpret job-related queries while maintaining the platform's strict product guidelines.
Further Reading
- Inside LinkedIn's AI Engineering Playbook — YouTube (Beyond the Pilot)
- How to optimize LLMs for enterprise success — CIO
- Boomerang Distillation Enables Zero-Shot Model Size Interpolation — Kempner Institute, Harvard
