Swift LLM Code Review Engineer

You will review AI-generated Swift code and assess how well it meets project guidelines. Your feedback will help AI systems produce better iOS code and explain technical decisions more clearly.

• Evaluate model-generated Swift and iOS code using project rubrics.
• Review source materials and model outputs against the guidelines.
• Judge correctness, maintainability, performance, and UI logic quality.
• Document evidence-based decisions and explain your reasoning clearly.
• Keep your reviews consistent and accurate across repeated tasks.

This is flexible, part-time remote contractor work. The project is expected to run for 1 to 3 months, with about 20 hours of work each week.

• Pay: $55 per hour.
• Estimated project earnings: $8,800 based on the listed schedule and duration.
• Schedule: About 20 hours per week with flexible scheduling.
• Open to candidates in Bangladesh, Bhutan, Brazil, Cambodia, Germany, India, Indonesia, Malaysia, Nepal, Pakistan, Singapore, Sri Lanka, Thailand, the Philippines, the United States, Timor-Leste, and Vietnam.
• At least 2 years of hands-on Swift and iOS development experience.
• Professional iOS engineering experience shipping or maintaining production mobile applications.
• Strong knowledge of Swift fundamentals, memory management, and automatic reference counting (ARC).
• Bachelor's degree in Computer Science or a closely related field.
• Significant experience using large language models for coding, debugging, or code review.
• Professional English fluency with at least C1 proficiency and strong technical writing skills.
• Experience reviewing app logic or mobile architecture; previous AI data training or model evaluation experience is strongly preferred.
• Experience with SwiftUI, UIKit, Combine, and modern iOS architectures such as MVVM is highly preferred.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training work uses human reviews, examples, and feedback to help artificial intelligence systems improve. People with strong technical knowledge are needed to assess difficult outputs and explain what good work looks like.