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Senior Recommender Systems Engineer

Eli Sato

Full-time · Job · Los_Angeles

About the role

Our recommendation engine drives 35% of all purchases on our marketplace — that's $12M in monthly GMV influenced by the models you'll build and improve. We serve 2 million active users, and each one sees personalized product rankings, bundle suggestions, and re-engagement offers powered by deep learning. You'll own the ranking and retrieval models: candidate generation using embedding-based retrieval, re-ranking with transformer architectures, and real-time personalization using streaming user signals. The scale is real — our serving layer handles 15,000 requests per second at p99 latency under 50ms. We need someone who's built recommendation systems at scale before. Not just trained models — designed the full system from candidate retrieval to re-ranking to serving. If you've done that and you're looking for a place where recommendations are the product, not a side feature, this is it.

Responsibilities

  • Own the end-to-end recommendation pipeline from retrieval to re-ranking
  • Design embedding-based candidate generation models for product discovery
  • Build real-time personalization features using streaming user signals
  • Optimize serving infrastructure for 15k QPS at sub-50ms latency
  • Run A/B tests and analyze recommendation impact on business metrics

Requirements

  • 5+ years building recommendation systems at scale
  • Deep PyTorch expertise with transformer and embedding architectures
  • Experience with Spark for large-scale feature engineering
  • Production experience with real-time serving at high QPS
  • Strong SQL skills and familiarity with A/B testing frameworks

Benefits

  • Fully remote — US West Coast hours
  • $160k–$200k base plus performance bonus
  • Generous equity package
  • Annual team summit

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch spark sql docker

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch spark sql docker aws pandas

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