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AI Training Data Pipeline Engineer

Ruby Santos

Full-time · Job · London

About the role

Models are only as good as their training data, and our training data comes from 200 annotators working across three time zones producing 15,000 labeled examples per day. Getting those labels from the annotation platform into training-ready datasets — cleaned, validated, versioned, and properly split — is a pipeline problem that nobody has owned until now. You'll build and maintain the data pipelines that connect our annotation tools to our training infrastructure. That means ingesting label exports, running quality checks, detecting annotator disagreement, computing inter-annotator agreement metrics, and publishing versioned datasets that ML engineers can pull with a single command. When labels are bad, your pipeline should catch it before a model trains on garbage. The data ops team is three people — you'll be the first dedicated pipeline engineer. Currently, ML engineers cobble together their own scripts for data prep; your job is to replace that with reliable, automated infrastructure. Expect to learn a lot about data quality, annotation management, and the messy realities of supervised learning.

Responsibilities

  • Build automated pipelines for ingesting and processing annotation data
  • Implement quality checks for label consistency and annotator agreement
  • Create versioned dataset publishing for ML training workflows
  • Detect and flag annotation quality issues before training begins
  • Build dashboards for annotation throughput and quality metrics
  • Maintain documentation for data pipeline architecture and usage

Requirements

  • 1+ year of Python development with SQL experience
  • Familiarity with data pipeline tools like Airflow or Prefect
  • Basic understanding of ML training workflows
  • Experience with cloud services — AWS preferred
  • Detail-oriented approach to data quality and validation

Benefits

  • Fully remote across UK/EU time zones
  • 25 days PTO
  • Private health insurance
  • £1,500 annual learning budget
  • Regular salary reviews with transparent progression framework

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python sql airflow docker aws

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Language

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Salary Range

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AI Expertise

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python sql airflow docker aws pandas

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