Meridial
Full Stack Engineering Specialist - Freelance AI Trainer Project
About this role
Freelance full stack engineering specialist needed to evaluate and annotate AI-generated code, providing expert feedback on correctness, efficiency, and best practices. This remote contract role involves reviewing AI outputs, identifying error patterns, and helping train large language models to become more capable problem-solving tools across industries.
What you'll do
- Review and annotate AI-generated code for logical and structural soundness
- Assess outputs for correctness, efficiency, scalability, and adherence to best practices
- Identify and document error patterns in AI-generated solutions
- Collaborate on refining prompts, evaluation methods, and engineering guidelines
- Provide expert technical feedback across the full stack
- Evaluate code quality across frontend and backend implementations
What they're looking for
- Full stack development (frontend and backend)
- JavaScript/TypeScript, Python, or Java
- React, Node.js, or Django
- SQL and NoSQL databases
- Cloud services and CI/CD pipelines
- Docker and Kubernetes containerization
- Problem-solving and attention to detail
- Clear communication
Benefits
- Remote work opportunity
- Flexible contract-based engagement
- Hourly rate of $8–$65 based on experience and location
- Opportunity to shape AI training and development
- Work with cutting-edge AI tools and models
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Meridial
Meridial trains and improves advanced AI models through expert evaluation and feedback across infrastructure, software engineering, machine learning, and language domains. The company hires experienced freelance specialists—including software engineers, ML experts, and language specialists—to test AI reasoning, identify failure modes, and provide detailed training data to enhance model capabilities.
View all jobs at MeridialLikely interview questions
- Walk us through your experience with both frontend and backend technologies. Which full stack projects are you most proud of, and why?
- Describe a time when you reviewed code and identified a significant logical or performance issue. How did you communicate the problem and recommend a solution?