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Senior SQL Engineer
SuperAnnotateCamrose, Alberta, CA- Halifax, NS (from $ 109,790 to $ 156,101 year)
- Parry Sound, ON (from $ 104,029 to $ 155,250 year)
- Montreal, QC (from $ 110,557 to $ 155,173 year)
- Old toronto, ON (from $ 69,880 to $ 153,998 year)
- Toronto, ON (from $ 70,000 to $ 153,998 year)
- Victoria, BC (from $ 83,481 to $ 152,265 year)
- Burnaby, BC (from $ 91,219 to $ 135,000 year)
- Vancouver, BC (from $ 73,125 to $ 131,242 year)
- Ottawa, ON (from $ 69,438 to $ 127,717 year)
- Calgary, AB (from $ 67,750 to $ 110,000 year)
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Senior SQL Engineer
SuperAnnotateCamrose, Alberta, CA- Full-time
- Remote
As a Senior SQL Engineer, you will work remotely on an hourly paid basis to review AI-generated SQL queries, database designs, and data-processing logic, as well as generate high-quality reference solutions and explanations for complex data problems. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in methodology, query structure, indexing strategy, or conceptual understanding; fact-check data-related claims; write clear, step-by-step explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on correctness, performance, and reasoning quality.
This is a fully remote, hourly paid contractor role with SME Careers, a fast-growing AI data services company and subsidiary of SuperAnnotate that provides AI training data to many of the world’s largest AI companies and foundation model labs. Your SQL and database engineering expertise will directly contribute to improving the world’s premier AI models used across industries.
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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