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AI Labeler Fatigue Detection Engineer

Ruby Santos

Full-time · Job · Chicago

About the role

Our annotation consensus and active learning tools already catch when labelers disagree with each other, but nobody catches when a single labeler's own quality quietly drops three hours into a long shift. Consensus agreement between two tired labelers can look fine even when both are making more mistakes than usual. This role builds a detection layer that watches individual labeler accuracy trends over the course of a shift and flags fatigue before it shows up in delivered data quality.

Responsibilities

  • Track individual labeler accuracy trends across shift duration
  • Build statistical models flagging likely fatigue-driven quality drops
  • Work with workforce operations to validate flagged fatigue patterns
  • Support rollout of break recommendations based on model output

Requirements

  • Some experience with SQL and time-series data analysis
  • Basic statistics background for trend detection
  • Interest in data quality and workforce operations problems
  • Comfortable working with sensitive workforce performance data

Benefits

  • Fully remote entry-level role
  • Structured mentorship program
  • Health insurance included
  • Clear growth path to mid-level analyst

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Categories

data science & analytics Automation & AI Workflows

Skills

python sql statistics pandas

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

Under $60k / year

AI Expertise

data science & analytics Automation & AI Workflows

Skills

python sql statistics pandas

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