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AI Taxonomy Drift Detection Engineer

Isaac Singh

Full-time · Job · UTC

About the role

We already have a knowledge graph covering millions of entities and relationships, refreshed nightly from a dozen upstream sources. What we don't have is a reliable way to know when the taxonomy underneath it starts drifting, when categories silently merge, split, or lose coverage as source data changes. You'll build monitoring that tracks taxonomy stability over time, flagging when entity categorization patterns shift in ways that suggest an upstream schema change or a labeling regression, before it quietly corrupts downstream applications. You'll work with the knowledge graph team to turn your alerts into an operational runbook they can act on without needing to understand the underlying drift detection statistics.

Responsibilities

  • Build drift detection monitoring for taxonomy and entity categorization
  • Own the Spark-based jobs that compute nightly stability metrics
  • Integrate alerts with the team's existing MLflow-tracked pipeline
  • Document runbooks so on-call engineers can triage alerts independently

Requirements

  • 3+ years in data engineering or ML monitoring roles
  • Experience with Spark for large-scale batch processing
  • SQL fluency and familiarity with MLflow or similar tracking tools
  • Comfortable working with graph or taxonomy-structured data

Benefits

  • Fully remote across time zones
  • Health coverage stipend
  • Home office budget
  • Flexible working hours

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Categories

MLOps & AI Infrastructure data science & analytics

Skills

python sql spark mlflow

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure data science & analytics

Skills

python sql spark mlflow

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