Legal Annotators - Portuguese (Portugal)
RWS TrainAILawFirst seen 9 Oct 2026
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- Where
- Portugal
- Language
- Portuguese, English
- Type
- Contract, remote
- Field
- Law
Posted on RWS TrainAI First seen 9 Oct 2026Checked 12 Oct 2026
From the RWS TrainAI listing
We are looking for Legal Annotators to support the training and evaluation of AI capabilities for contract management platforms in Portuguese (Portugal).
- Job Type: Freelance
- Work Schedule: Part-time - 25+ flexible hours per week
- Start Date: Immediately
- Duration: TBC
- Rate: 22.95 USD/hour
Help Shape the Future of Legal AI
Are you a legal professional, contract specialist, or law graduate seeking flexible remote work? Are you interested in shaping the development and safety of advanced AI models designed to automate contract management and analysis?
What You’ll Do:
The role involves performing specialized legal data-related tasks across international business contracts, including:
- Multilingual legal-domain data collection, evaluation, and annotation.
- Analyzing clauses, obligations, and terms within complex business agreements.
- Participating in onboarding, calibration sessions, and targeted quality reviews.
- Collaborating on multi-use-case workflows within secure client environments.
What We’re Looking For:
- Legal Expertise: Contract or legal domain expertise, with strong legal interpretation and contract-review skills (especially regarding international and cross-jurisdictional contracts).
- Language Proficiency: Fluency in English alongside the relevant target language.
- AI & Data Capabilities (Preferred): Prior data-annotation experience or familiarity with machine learning data evaluation and quality assurance tasks.
What We Offer:
- Flexible schedule
- Opportunity to earn extra income
- Timely payments
#LI-CAN17
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Practice questions and what each platform says about its screening for this kind of role:
Terms in this listing
- Data collection: Projects where you create new data, such as photos, videos, recordings or written samples, instead of judging existing data.
- Annotation: Adding labels or notes to data such as text, images, audio or video so a model can learn from it.
- Calibration: Exercises where raters do the same tasks and compare results with the expected answers, so everyone applies the guidelines the same way.
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