LLM Quality & AI Content Intern – Paris

Back Market

Paris

Hybride

EUR 12 000 - 18 000

Plein temps

Il y a 4 jours
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Avantages offerts par ce poste

Hybrid work environment
2 remote days per week
Mentorship programs

Résumé du poste

Back Market is seeking an AI Quality & Evaluation Intern to work with the QA Lead on monitoring Fin’s performance, reviewing conversations, and turning findings into concrete improvements. You will collaborate with content, product, data, and engineering teams to enhance the quality evaluation process.

The role involves analyzing performance across markets and languages, tracking actions, and sharing actionable feedback while supporting multilingual and LLM evaluation efforts.

Qualifications

  • Pursuing a Master 2 in Data Science, Applied Mathematics, Statistics, or a related engineering field.
  • Strong analytical and problem-solving abilities are required.
  • Comfortable working with data and investigating anomalies.

Responsabilités

  • Monitor Fin’s quality monitors and scorecards.
  • Review conversations to identify issues and recurring defects.
  • Prepare weekly quality review summaries with risks and root causes.
  • Analyze Fin’s performance across markets, languages, and rollout phases.
  • Collaborate with product, data, engineering, and content teams to drive improvements.
  • Document and version the evaluation framework clearly.

Connaissances

Analytical skills
Problem solving
Data analysis
English communication
Interest in AI

Formation

Master 2 in Data Science / Applied Math / Stats

Outils

Python
SQL

Description du poste

Back Market is seeking an AI Quality & Evaluation Intern to work with the QA Lead on monitoring Fin’s performance, reviewing conversations, and turning findings into concrete improvements. You will collaborate with content, product, data, and engineering teams to enhance the quality evaluation process.

The role involves analyzing performance across markets and languages, tracking actions, and sharing actionable feedback while supporting multilingual and LLM evaluation efforts.

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