Ernesto Rios

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Top Label Types

Classification

Translation Localization

RLHF

Computer Programming Coding

Prompt Response Writing SFT

Freelancer Overview

I am a software and integration engineer with extensive hands-on experience in AI model evaluation, data engineering, and process automation. My work at Scale AI involved optimizing Large Language Models (LLMs) using Reinforcement Learning from Human Feedback (RLHF), designing and validating complex prompts, and establishing ground truth data for advanced technical queries in areas such as programming, math coding, and data analysis. I am skilled in Python, SQL, cloud platforms (AWS, GCP, Azure), and DevOps tools like Docker and Terraform. I have engineered real-time data pipelines, automated data workflows, and developed grading rubrics to ensure the quality, accuracy, and reliability of AI-generated outputs. My background spans code evaluation, prompt design, and business intelligence, and I thrive in collaborative, fast-paced environments where precision and innovation in AI training data are paramount.

Labeling Experience

AI Trainer

Scale AIComputer Code ProgrammingClassificationTranslation Localization

Spearheaded the evaluation and optimization of Large Language Models (LLMs) via Reinforcement Learning from Human Feedback (RLHF), engineering high-complexity Chain-of-Thought (CoT) prompts to train models on advanced logic and reasoning paths. Rigorously validated State-of-the-Art (SOTA) solutions across multiple programming languages (Python, SQL, C++), performing deep-dive debugging and technical analysis to ensure AI-generated code is production-ready, secure, and efficient. Developed and solved complex coding challenges in Math Coding, Data Analysis, and 2D/3D Animation environments, establishing the "Ground Truth" for specialized technical queries that require high-precision engineering standards. Applied software engineering best practices (DevOps, CI/CD logic) to evaluate AI performance in deployment scenarios, designing granular grading rubrics to measure code quality, correctness, and style against strict industry benchmarks.

Spearheaded the evaluation and optimization of Large Language Models (LLMs) via Reinforcement Learning from Human Feedback (RLHF), engineering high-complexity Chain-of-Thought (CoT) prompts to train models on advanced logic and reasoning paths. Rigorously validated State-of-the-Art (SOTA) solutions across multiple programming languages (Python, SQL, C++), performing deep-dive debugging and technical analysis to ensure AI-generated code is production-ready, secure, and efficient. Developed and solved complex coding challenges in Math Coding, Data Analysis, and 2D/3D Animation environments, establishing the "Ground Truth" for specialized technical queries that require high-precision engineering standards. Applied software engineering best practices (DevOps, CI/CD logic) to evaluate AI performance in deployment scenarios, designing granular grading rubrics to measure code quality, correctness, and style against strict industry benchmarks.

2025

Education

Work History