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
LinkedIn’s engineers have been wrestling with a familiar problem: turning the platform’s massive talent data into a conversational assistant that respects its strict product guidelines. Early experiments leaned heavily on prompt‑engineering, but the team quickly found the approach unwieldy—responses drifted, and latency spiked when the model tried to juggle a job posting, a résumé and a recruiter’s nuance in a single turn. The setback forced a pivot toward leaner architectures.
By embracing open‑source, small‑scale models and a technique that pools knowledge from several teacher networks, the group managed to keep inference fast enough for real‑time use while still honoring LinkedIn’s policy constraints. The result, according to internal reports, was a system that could parse individual job queries, candidate profiles and job descriptions on the fly. It’s this shift that sets the stage for the next point:
Why multi-teacher distillation was a ‘breakthrough’ for LinkedIn…
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.
LinkedIn’s latest LLM owes its performance to a blend of multi‑teacher distillation and compact model architectures. Prompting, the team says, simply didn’t cut it. By training several teacher models on distinct facets of job data and then distilling their knowledge into a lean student, engineers achieved the latency and accuracy required for real‑time recommendations.
The resulting system can parse a candidate’s profile, a job description, and a user’s query in a single pass, staying aligned with LinkedIn’s product policy. Yet the article offers no benchmark data, leaving open how these gains compare to larger, more generic models. Could this replace larger models?
Moreover, the long‑term maintenance of multiple teachers may introduce complexity that the piece does not address. The claim of a ‘breakthrough’ rests on internal metrics; external validation remains uncertain. If the approach scales, it could inform other recommendation pipelines, but whether small models can consistently replace prompting across domains is still unclear.
Future internal tests may clarify its broader applicability.
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
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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.