AI Work Central

SQL Engineer

SME CareersCodingFirst seen 9 Oct 2026

up to$50/hr as listed by SME Careers, USDEstimate a week at this rate
Apply on SME Careers

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Where
17 countries

United States, Germany, Brazil, India, Bangladesh, Bhutan, Indonesia, Cambodia, Sri Lanka, Malaysia, Nepal, Philippines, Pakistan, Singapore, Thailand, Timor Leste, Vietnam

Language
English
Type
Contract, remote
Field
Coding

Posted on SME Careers First seen 9 Oct 2026Checked 12 Oct 2026

From the SME Careers listing

As a 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.

Important:

There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

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.

Requirements

  • Bachelor’s degree or higher in Computer Science, Information Systems, Engineering, Mathematics, or a closely related technical field.
  • 7+ years of professional experience working with SQL in production environments as a Database Engineer, SQL Developer, Data Engineer, or similar role.
  • Expert-level proficiency in SQL, including complex joins, window functions, aggregation strategies, and query plan analysis.
  • Demonstrated experience with database performance tuning, indexing strategies, and troubleshooting concurrency or locking issues.
  • Hands-on experience designing relational data models, schemas, and data warehouse structures (star and snowflake schemas).
  • Strong experience with ETL/ELT processes and tooling, as well as working with large-scale data in modern cloud or on-prem data platforms.
  • Minimum C1 English proficiency (written and spoken), with the ability to write clear technical explanations and follow detailed English-language guidelines.
  • Comfort with version control, CI/CD practices, and collaborative workflows for managing database changes.
  • Previous experience with AI data training, annotation, or evaluating AI-generated technical content is a strong plus.
  • Highly detail-oriented and systematic, with a methodical approach to evaluating reasoning quality and identifying subtle data or query issues.

Text above is the platform's own listing, shown as published. Check the details on SME Careers before applying.

Prepare for the assessment

Practice questions and what each platform says about its screening for this kind of role:

Terms in this listing

  • Prompt: The input given to a model: a question, an instruction or a conversation so far.
  • Relevance: How well a result or answer matches what the user was looking for.
  • Guidelines: The project's written instructions that define how to do a task and how to judge answers. On most projects they take precedence over personal preference.
  • Annotation: Adding labels or notes to data such as text, images, audio or video so a model can learn from it.

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