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AI Content Freshness Decay Scoring Engineer

Aaliyah Okafor

Full-time · Job · New_York

About the role

LangChain isn't part of this stack, but our real-time interest graph and session-based recommendation models are, and they share one blind spot: nothing tells them when a previously great recommendation has gone stale. You will build a freshness decay scoring layer that estimates how quickly a given piece of content's relevance fades for a given audience segment, feeding directly into recommendation ranking as a decay factor. You'll work closely with the cold-start recommendation team, since freshness decay and cold-start scoring need to stay consistent when content transitions from new to established to stale.

Responsibilities

  • Build a freshness decay scoring layer estimating content relevance decay over time
  • Integrate decay scores into existing recommendation ranking pipelines
  • Coordinate with the cold-start team to keep scoring consistent across content lifecycle stages
  • Run experiments measuring engagement impact of decay-aware ranking
  • Maintain Spark pipelines computing decay scores at scale

Requirements

  • 3+ years in applied recommendation systems
  • Strong Python, SQL, and Spark experience
  • Comfortable running and interpreting online experiments
  • Experience with time-aware ranking or relevance decay a plus

Benefits

  • Fully remote position
  • Competitive salary and equity
  • Health and dental insurance
  • Flexible PTO

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Categories

AI Product & Strategy data science & analytics

Skills

python sql spark

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

AI Product & Strategy data science & analytics

Skills

python sql spark

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