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Junior AI Annotation Batch Quality Sampler

Ruby Santos

Freelance · Job · New_York

About the role

Data annotation quality determines model quality — but you can't manually review every annotation in a batch of 100,000 labeled examples. Statistical sampling is the answer: review a representative sample, estimate batch quality from the sample, and flag batches that fall below quality thresholds before they enter training pipelines. The sampling strategy matters: random sampling finds average quality but misses systematic errors concentrated in specific label categories or annotator cohorts. This is a freelance role — 20-25 hours per week — building the intelligent annotation batch sampling system for our data labeling platform. You'll implement sampling strategies that over-represent potentially problematic annotations: examples where annotators disagreed, examples near decision boundaries, examples from annotators with historically lower accuracy. Senior data scientists define the sampling theory; you implement the execution system. You'll build the sampling pipelines, implement the quality estimation logic from sample review outcomes, and create the batch quality reports that annotation managers use to accept, reject, or selectively re-review batches before they ship to ML teams.

Responsibilities

  • Implement intelligent annotation batch sampling strategies targeting high-risk annotation subsets
  • Build sampling weight computation based on annotator history, label distribution, and disagreement signals
  • Create batch quality estimation from sample review outcomes with confidence intervals
  • Develop batch quality reports giving annotation managers clear accept-or-rereview decisions
  • Implement sampling strategy comparison evaluating which approaches detect quality issues most efficiently
  • Monitor sampling system performance tracking how often flagged batches are confirmed as low quality

Requirements

  • 1+ year of Python and SQL development experience
  • Basic familiarity with statistical sampling concepts
  • Interest in data quality, annotation workflows, or ML data infrastructure
  • Attention to statistical accuracy in quality estimation
  • Reliable async communication for a freelance engagement

Benefits

  • Fully remote freelance — 20-25 hours per week
  • Flexible schedule
  • Competitive hourly rate
  • Mentorship from senior annotation quality engineers
  • Potential to expand scope

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Categories

AI Product & Strategy data science & analytics

Skills

python sql pandas statistics scikit-learn

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Job Type

Freelance

Level

Junior

Language

English

Salary Range

Under $60k / year

AI Expertise

AI Product & Strategy data science & analytics

Skills

python sql pandas statistics scikit-learn

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