# Distilling Long-Tail User Behavior into Scalable Embeddings for Job Search

DevFeed: [Distilling Long-Tail User Behavior into Scalable Embeddings for Job Search](<https://devfeed.tech/articles/distilling-long-tail-user-behavior-into-scalable-embeddings-for-job-search-29995.md>)

Original publisher: [Read original article](<https://engineering.indeedblog.com/blog/2026/06/distilling-long-tail-user-behavior-into-scalable-embeddings-for-job-search/>)

Author: Marsan Ma

Published: 2026-06-03T23:28:10Z

Content type: article

Language: en

Sources: [Indeed](<https://devfeed.tech/sources/indeed.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Job](<https://devfeed.tech/topics/job.md>)

Tags: [big-data](<https://devfeed.tech/tags/big-data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [feature-store](<https://devfeed.tech/tags/feature-store.md>), [job-search](<https://devfeed.tech/tags/job-search.md>), [latency](<https://devfeed.tech/tags/latency.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [performance](<https://devfeed.tech/tags/performance.md>), [ranking](<https://devfeed.tech/tags/ranking.md>)

## AI overview

Indeed describes a user behavior modeling system for job search that learns from long-term user histories offline, distills them into fixed-length embeddings, and serves them through a feature store for use by online ranking and recommendation models. The approach is designed to preserve rich behavioral signals while meeting latency and cost constraints.

## Source excerpt

Authors : Marsan Ma, Nikhil Lopes, Raj Amrit, Hong Lu, Dipankar Biswas, Trent KyonoLeadership: Iris Wang, Madhu Kurup Recommendation and ranking systems power many of the most important experiences on large internet platforms. Yet the models that run in production are rarely the largest models we can train. They are usually compact, latency-sensitive supervised models [...]