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AI Learning-to-Rank Feature Engineer

Kyle Park

Full-time · Job · Toronto

About the role

Spark for offline features, Kafka for real-time signals, and a gradient-boosted ranker that serves around 30 million queries a day. That is the core of the search stack we run for mid-sized e-commerce and documentation sites. The ranker itself is fine; what limits it is the feature set, which hasn't changed much in a year. You'll design, compute and test new ranking features: click-through priors, freshness decay, query-document semantic similarity, user session context. Every feature goes through an offline NDCG check and then an online interleaving test, and you'll own both. It's interesting work because small features compound, and you'll see the effect of your changes in live metrics within days.

Responsibilities

  • Propose and build new ranking features using offline and streaming data
  • Run offline relevance evaluations and online interleaving experiments
  • Keep feature pipelines efficient as query volume grows
  • Partner with search product managers to connect features to customer outcomes

Requirements

  • Hands-on experience with learning-to-rank or recommendation systems
  • Strong SQL and PySpark skills
  • Understanding of ranking metrics like NDCG and MRR
  • Familiarity with streaming data from Kafka or similar

Benefits

  • Remote across Canada and US Eastern time
  • Stock options
  • Health and wellness spending account
  • Quarterly team offsites

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python spark scikit-learn sql kafka

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Job Type

Full-time

Level

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Language

English

Salary Range

$100k – $130k / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python spark scikit-learn sql kafka

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