Legal Engineer - AI Quality (Internship)
About the role
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. Keep the feedback documentation current on Confluence.
Map the competitive landscape
Track legal tech products shipping notable AI capabilities and maintain a clear-eyed view of the market: what they claim, what they actually do, where we lead and where we don't.
Document and enable
Write and maintain the Confluence reference on our AI features - capabilities, limitations, appropriate use cases - for Product, Sales, Customer Success and Support. If a colleague can explain the limits of a feature to a client without asking the ML team, you have done this well.
Who we're looking for
A gap year student (Bac +4/+5) matching one of two profiles:
• Legal background with a real pull toward technology. You have a law degree, and you find yourself more curious about how the tool works than about the case law.
• Technical or engineering background with genuine interest in law. You can read a model output critically, and you want to work in a domain where precision has consequences.
In both cases, the person we are looking for is:
• Curious, autonomous and rigorous - able to own a workstream without being managed through it
• Analytically minded: structures information, spots inconsistencies, synthesises clearly
• An excellent communicator in French and English, written and spoken
• Comfortable in a fast-paced environment where the roadmap moves
Required skills
• Solid legal knowledge, or a demonstrated ability to get up to speed on legal concepts fast
• Fluent French and English - English is the working language inside the ML team
• Proficiency with productivity tools: Confluence, Jira (or equivalent), Slack, Excel / Google Sheets
• Nice to have: familiarity with LLMs, prompt engineering, or AI evaluation concepts. Hands-on experience with AI tools applied to legal work is a strong plus.
What you'll get out of it
Direct exposure to how AI features are built, measured and shipped in a production legal tech product - inside the ML team rather than adjacent to it. Legal engineering is becoming its own career track, and this is a year of it on a real product.
Recruitment process
Two steps, and we move quickly.
- Introductory call (20-30 min, phone or video)
A conversation with [hiring manager / talent team] on your background, what draws you to this intersection of law and AI, and the practical basics (dates, duration, school requirements). Partly in English.
- Use case (take-home + debrief)
We send you a short, concrete exercise: a set of AI outputs on legal documents to assess. You tell us what is wrong, how you would measure it systematically, and what you would report back to the ML team. Expect around [2 hours] of work. We then discuss it together for 45-60 minutes with the ML team and Product. We are not looking for the right answer - we are looking at how you reason, structure a problem, and defend a judgement call.
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