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Trainer • simcoe on
Remote Legal Expert - AI Trainer
code4Simcoe, CanadaMedical Science Liaison - Ontario - Specialty ProductsMedical Science Liaison
Bausch & LombOntario, Canada- Promoted
Team Member
Tim HortonsSimcoe, ON, Canada- Saanich, BC (from $ 46,118 to $ 390,000 year)
- Bowmanville, ON (from $ 103,188 to $ 325,000 year)
- Stratford, ON (from $ 54,014 to $ 250,746 year)
- Prince Edward, ON (from $ 49,785 to $ 150,912 year)
- Oakville, ON (from $ 45,016 to $ 138,938 year)
- Caledon, ON (from $ 48,750 to $ 136,838 year)
- Kelowna, BC (from $ 45,533 to $ 127,060 year)
- Langley, BC (from $ 46,293 to $ 124,800 year)
- Whitby, ON (from $ 60,000 to $ 121,950 year)
- Barrie, ON (from $ 43,997 to $ 121,625 year)
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Remote Legal Expert - AI Trainer
code4Simcoe, Canada- Full-time
- Remote
In this hourly, remote contractor role, you will review AI-generated legal analyses and / or generate expert legal content, evaluating reasoning quality and step-by-step issue-spotting while providing precise written feedback. You will assess answers for accuracy, clarity, and adherence to the prompt; identify errors in legal methodology or doctrine; fact-check citations and stated rules; write high-quality explanations and model solutions; and rate and compare multiple responses based on correctness and reasoning quality. This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your legal expertise directly helps improve the world’s premier AI models by making their legal reasoning more accurate, reliable, and clearly explained.
Key Responsibilities :
- Develop AI Training Content : Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
- Optimize AI Performance : Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
- Ensure Model Integrity : Test AI models for potential inaccuracies or biases, validating their reliability across use cases.
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