What if your Lua expertise could directly shape how AI writes code for millions of developers and creators around the world?
If you've shipped Lua in production — whether in game development, Roblox Studio, or elsewhere — this is a rare opportunity to apply that hands-on expertise in a cutting-edge AI context. Fully remote, flexible hours, and meaningful work that actually moves the needle.
Why Join Us
Work on cutting-edge AI projects alongside leading research labs.
Fully remote and flexible — work when and where it suits you.
Freelance autonomy with the structure of meaningful, task-based work.
Make a direct, tangible impact on how AI understands and writes Lua code.
Potential for ongoing work and contract extension based on performance.
Responsibilities
Review and evaluate AI-generated Lua code for correctness, efficiency, and adherence to best practices.
Identify logical errors, performance bottlenecks, and implementation gaps.
Provide clear, structured written feedback that helps improve AI outputs.
Complete diverse, task-based technical assignments with precision and consistency.
Follow detailed project guidelines and uphold high quality standards across all deliverables.
Contribute directly to improving how AI reasons about and generates Lua code.
Required skills
Advanced proficiency in Lua programming — you've built and shipped real things with it.
Sharp attention to detail and comfort following structured technical instructions.
Preferred skills
Professional background in game development.
Experience reviewing or mentoring other developers' code.
Familiarity with performance optimization and debugging practices in Lua environments.
Prior exposure to code review workflows or quality evaluation.
Experience and education
Experience working with Roblox Studio or the broader Roblox platform.
Language requirements
Ability to articulate technical feedback clearly in written English.
Thriveth makes AI data-training work easier to find, understand, and navigate. We replace uncertainty with clear opportunities, realistic expectations, and insights from real application journeys.
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