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Sql database developer Jobs in Killeen, TX
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Sql database developer • killeen tx
Remote Senior SQL Engineer - AI Trainer
SuperAnnotateKilleen, Texas, USApplication Developer III
First National Bank TexasKilleen, TX- Promoted
Remote Side Hustle Developer
Finance BuzzNolanville, Texas, US- veterinarian (from $ 75,000 to $ 225,000 year)
- software engineer (from $ 77,500 to $ 203,000 year)
- medical director (from $ 30,000 to $ 200,000 year)
- data scientist (from $ 136,600 to $ 200,000 year)
- pet care (from $ 38,480 to $ 200,000 year)
- pet (from $ 30,000 to $ 200,000 year)
- architect (from $ 75,000 to $ 187,008 year)
- hospital (from $ 60,444 to $ 182,044 year)
The average salary range is between $ 90,000 and $ 140,000 year , with the average salary hovering around $ 110,000 year .
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Remote Senior SQL Engineer - AI Trainer
SuperAnnotateKilleen, Texas, US- 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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