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Legal Engineer - AI Quality (Internship)

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EMP:Technology
Paris - La Défense
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Legal Engineer Intern — AI Quality & Evaluation  Internship · Team: Machine Learning · [Paris — hybrid] · 6 months · Start: ASAP · Working languages: English & French   .

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the role  We are embedding AI across every DiliTrust module, and the hardest question is not "does it run?" but "is it good enough for a legal professional to rely on?" Answering that requires someone who understands both the model and the law.  That is this role

You will join the ML team as the legal voice inside the build loop, translating legal expertise into the test sets, evaluation criteria and documentation that determine whether our AI features ship.

You will work day to day with ML engineers and Product Managers, and your findings will directly shape what gets released and what goes back for rework.

  This is a legal engineering position, not a legal practice one.

You will not draft contracts; you will define what a correct answer looks like on a contract, at scale, and hold the system to it.

  What you'll do  Design and run AI evaluations (Lini)  Build evaluation campaigns for Lini across all modules (CLM, Board Portal, Legal Entity Management, Matter Management).

Define what "correct" means for each feature: scoring rubrics, acceptance thresholds, and the edge cases that matter to a lawyer but are invisible to a metric.

Run the campaigns, analyse the results, and escalate quality issues to the ML team with a clear diagnosis rather than a bug report.

  Own the Golden Data  Build, curate and maintain the reference test sets the team measures against.

Source representative legal documents, establish the ground truth, and keep coverage honest as features evolve.

This dataset becomes the team's working definition of quality, and it will be yours.

  Turn client feedback into product signal  Collect and structure the feedback on AI features gathered by Customer Success.

Build a failure taxonomy instead of a list of complaints, so that recurring weaknesses become prioritisable work items.

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