Code Data Annotation Quality Specialist

Mistral AI Mistral AI · AI Frontier · Paris, France · Research

Mistral AI is seeking a Data Quality Specialist to join their Human Data Annotation team. This hybrid role involves reviewing and auditing code annotations against rubrics to ensure data quality for AI model training and evaluation, and also building, maintaining, and troubleshooting internal tooling for annotators. The role requires strong analytical skills, attention to detail, proficiency in programming languages, and experience with code agents and developer environments.

What you'd actually do

  1. Generate and validate high-quality data annotations, based on guidelines and continuous feedback, for the development and evaluation of AI models
  2. Surface systemic issues, edge cases, and gaps in guidelines back to annotation operations and technical stakeholders
  3. Produce annotations yourself when needed, modeling the quality bar expected of the team
  4. Build and maintain internal tools and automation that streamline annotator workflows such as visualization dashboards, batch configuration scripts, output management utilities, and similar
  5. Troubleshoot environment, tooling, and CLI/git issues for annotators on their local machines, liaising with IT and engineering as needed

Skills

Required

  • strong analytical skills
  • keen eye for detail
  • Proficient in at least one programming language (e.g. Python, JavaScript, or similar)
  • Able to apply consistent judgment against a rubric and surface edge cases, ambiguities, or gaps in guidelines
  • Sustained focus and accuracy on detail-oriented, high-volume review work
  • Comfortable working in a Unix-like terminal: shell basics, package managers, environment setup, and git workflows (branches, merges, resolving conflicts)
  • Able to troubleshoot local development environment issues (dependencies, virtual environments, paths, permissions) across common operating systems
  • Professional proficiency in English

Nice to have

  • Prior experience in data annotation for AI/ML, especially LLM training (SFT, RLHF, preference data), evals/benchmarks, or agentic data
  • Experience building an annotation team through interviews and training
  • Experience supporting technical users or troubleshooting developer environments (internal tools support, DevRel, teaching assistant for coding courses, etc.)
  • Fluency across multiple programming languages, or domain depth in one of: frontend, backend, DevOps, MLOps, data engineering
  • Familiarity with rubric-based evaluation concepts, inter-annotator agreement, or quality measurement for human-labeled data
  • Experience developing, deploying, and managing internal tooling or automation scripts

What the JD emphasized

  • reviewing and auditing code annotations
  • building, maintaining, and troubleshooting the internal tooling
  • Hands-on experience using code agents
  • Proficient in at least one programming language
  • Comfortable working in a Unix-like terminal
  • troubleshoot local development environment issues

Other signals

  • data quality for AI models
  • reviewing and auditing code annotations
  • building and maintaining internal tooling