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AI Annotation Quality Engineer

Ruby Santos

Full-time · Job · Chicago

About the role

ML models are only as good as their labels. A classification model trained on annotations where 15% of the labels are wrong will learn the mistakes. Our annotation quality platform detects labeling errors before they corrupt model training — using inter-annotator agreement analysis, automated consistency checks, and ML-assisted quality scoring. You'll build the quality measurement systems: statistical methods for estimating true label quality from multiple annotator judgments, automated detection of annotation patterns that indicate confusion or fatigue, and the calibration tools that help annotation teams maintain consistency across millions of labeled items. Annotation quality is a meta-problem — you're building ML to improve the data that trains other ML. The feedback loops are complex: quality models need quality labels themselves, and measuring quality requires ground truth that's expensive to obtain. You'll develop creative approaches to these bootstrapping challenges.

Responsibilities

  • Build annotation quality measurement using inter-annotator agreement analysis
  • Design automated detection of labeling errors from confusion and fatigue patterns
  • Develop calibration tools maintaining annotation consistency at scale
  • Create quality scoring models estimating true label accuracy
  • Generate quality reports guiding annotation team training and process improvements

Requirements

  • 3+ years in data quality, annotation management, or ML evaluation
  • Strong statistical skills for agreement metrics and quality estimation
  • Python and pandas proficiency for annotation data analysis
  • SQL skills for querying annotation databases
  • Understanding of how label quality affects downstream model performance

Benefits

  • Fully remote — US Central time zone
  • Health insurance with dental
  • $2,000 learning budget
  • Solving a foundational problem in ML data quality

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Categories

data science & analytics MLOps & AI Infrastructure

Skills

python pandas sql statistics scikit-learn

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

data science & analytics MLOps & AI Infrastructure

Skills

python pandas sql statistics scikit-learn

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