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Quantitative analyst Jobs in Glace Bay, NS
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Quantitative analyst • glace bay ns
Remote R Engineer - AI Trainer
SuperAnnotateGlace Bay, Nova Scotia, CAIntermediate Programmer Analyst
Medavie Blue CrossNova Scotia, CA- East York, ON (from $ 85,000 to $ 143,999 year)
- Calgary, AB (from $ 85,000 to $ 140,997 year)
- Quebec City, QC (from $ 85,000 to $ 140,721 year)
- North York, ON (from $ 85,000 to $ 138,000 year)
- Vancouver, BC (from $ 85,844 to $ 125,901 year)
- Pickering, ON (from $ 120,621 to $ 120,706 year)
- St. John's, NL (from $ 85,000 to $ 120,603 year)
- Toronto, ON (from $ 99,916 to $ 117,975 year)
- Bradford West Gwillimbury, ON (from $ 96,250 to $ 116,810 year)
- Old toronto, ON (from $ 99,828 to $ 115,355 year)
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Remote R Engineer - AI Trainer
SuperAnnotateGlace Bay, Nova Scotia, CA- Full-time
- Remote
As a remote, hourly paid R Engineer, you will review AI-generated responses and generate high-quality R and data-analysis-focused content, evaluating the reasoning quality and step-by-step problem-solving behind each solution. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in statistical methodology or data-wrangling workflows; fact-check analytical results; write expert-level explanations and model solutions that demonstrate correct use of R; and rate and compare multiple AI responses based on correctness and reasoning quality. This contract role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate that provides AI training data for many of the world’s largest AI companies and foundation model labs, directly helping to improve the world’s premier AI models.
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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