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AI Cold-Start Recommendation Engineer

Aaliyah Okafor

Full-time · Job · Los_Angeles

About the role

The models you ship in this role decide what a brand-new user sees in their very first ten minutes on a client's app, before any interest graph or session data exists to work with. Our session-based and interest graph recommendation systems perform well once a user has some history, but that first-session experience still leans on generic popularity lists that convert poorly. You'll build cold-start models that make a strong first guess from onboarding signals alone, then hand off cleanly to the personalized systems once real behavior data arrives.

Responsibilities

  • Build cold-start recommendation models using onboarding and contextual signals
  • Design the handoff logic between cold-start and personalized recommendation systems
  • Run experiments measuring first-session engagement and conversion impact
  • Work with the broader personalization team on shared feature infrastructure

Requirements

  • Strong background in recommender systems, especially cold-start problems
  • Experience with PyTorch and scikit-learn for ranking models
  • Comfortable designing and analyzing online experiments
  • Ability to work across onboarding, product, and personalization teams

Benefits

  • Fully remote position
  • Competitive salary and equity
  • Health and dental insurance
  • Annual recommender systems conference budget

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn statistics

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Full-time

Level

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Language

English

Salary Range

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AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn statistics

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