🤖 Teaching Machines to Understand: My Time at Turing (for Meta)
Role: AI Data Trainer (LLM Language Specialist)
Company: Turing (contracted by Meta)
Location: Remote / Global
Timeframe: 2023
LLMs: LLaMA, Guava
Client: Meta 🧠
🚀 Entering the World of Large Language Models
In 2025, I had the unique opportunity to join Turing, one of the world’s leading platforms for remote tech talent, for a short but impactful project. This wasn’t just any job — I was brought in to help train large language models (LLMs) developed by Meta, including LLaMA.
My role combined linguistics, logic, UX thinking, and technical precision — perfect for someone who loves crossing boundaries between disciplines.
🧠 What I Did
• Crafted training and evaluation prompts for cutting-edge LLMs
• Assessed and improved AI-generated responses for logic, accuracy, empathy, and fluency
• Applied instruction tuning techniques to optimize LLM understanding of nuanced prompts
• Delivered culturally sensitive, human-like interactions — tailored for global audiences
• Collaborated asynchronously with an international team of experts
🌍 Why It Mattered
The work we did was part of Meta’s ongoing mission to create safe, high-performing, multilingual AI models.
While my time on the project was brief, the impact of the work continues to ripple out in:
🧬 Better AI understanding of user intent
🌐 Safer, smarter multilingual AI responses
💬 More empathetic, accurate conversation agents across Meta’s platforms
🛠️ Key Takeaways
This experience gave me a glimpse into the backstage of AI development, where real people shape how machines understand us.
✅ Precision Writing: Learned how small linguistic shifts could drastically change model outputs
✅ System Thinking: Understood how model tuning affects end-user behavior
✅ Cross-Disciplinary Insight: Combined UX, linguistics, and logic in high-stakes environments
✅ Global Remote Workflow: Excelled in asynchronous, multicultural, and deadline-sensitive project teams
🔁 Reflection
Teaching an AI to think more like a human is no small task. It takes clarity, empathy, and a strong grasp of language systems. This project reminded me of the power behind clean design, good data, and human judgment — all wrapped into a system that millions may interact with.
It was short, sharp, and unforgettable. And it’s made me even more excited about the intersection of people, systems, and smart technology.