Backend Developer - AI Trainer (Ecuador)

📋 Description

• • Design and implement multi-file coding tasks involving bug resolution, feature implementation, refactoring, and test generation for production-realistic software engineering scenarios.
• • Write clear, precise natural-language specifications and reference implementations that accurately define coding problems and expected outcomes.
• • Develop and extend comprehensive unit and integration test suites using frameworks such as pytest, Jest, JUnit, xUnit, or Go testing to validate code correctness and edge case handling.
• • Review peer-generated coding tasks for correctness, clarity, realism, and alignment between specifications and implemented solutions.
• • Identify edge cases, ambiguities, logical inconsistencies, and potential failure modes in both task designs and peer submissions.
• • Ensure strict alignment between task specifications, reference code implementations, and expected outputs across all evaluation stages.
• • Work with large, complex, multi-file codebases to assess code quality, maintainability, and adherence to engineering best practices.
• • Participate in code review processes to evaluate code structure, readability, performance, and adherence to project standards.
• • Contribute to production migrations and code refactoring tasks by analyzing existing systems and proposing viable improvements.
• • Maintain high standards of technical accuracy and consistency in all deliverables to support AI training datasets.
• • Operate independently on a project-based, part-time consultant basis with full control over schedule and workflow.
• • Deliver high-quality, production-grade coding tasks that reflect real-world software engineering challenges encountered in industry environments.
• • Provide structured, actionable feedback on peer submissions that highlights strengths, weaknesses, and opportunities for improvement.
• • Generate validation artifacts including test results, coverage metrics, and failure case analyses to support model evaluation.
• • Ensure all work meets strict technical standards for realism, scalability, and reproducibility to be used in training advanced AI models.
• • Work remotely with no requirement for office attendance; all tools and infrastructure must be provided by the contributor.
• • Collaborate asynchronously with a global team of AI trainers and engineers without direct supervision.
• • Accept compensation on a per-project basis, with payment calculated based on estimated completion time and deliverable quality.
• • Maintain the ability to concurrently provide services to other vendors or employers, subject to their respective policies and contractual allowances.