Dérian Fernández

Elevating Model Performance through High-Precision Human Feedback As an AI Training Specialist, I focus on the critical bridge between raw machine output and high-quality, safe, and helpful human interaction. My goal is to ensure your models are not only intelligent but also factually accurate, logically sound, and perfectly aligned with human intent. My Core Competencies in RLHF & Data Labeling: Preference Ranking (RLHF): Expertly evaluating model outputs based on custom rubrics, focusing on helpfulness, honesty, and harmlessness (HHH). Hallucination Mitigation: Rigorous fact-checking and grounding to eliminate "AI hallucinations" and ensure 100% verifiability. Supervised Fine-Tuning (SFT): Drafting "Golden Responses" that serve as high-quality training data for specific prompts. Linguistic Nuance & Safety: Identifying subtle biases, toxicity, or logical fallacies that automated filters might miss. Structured Data Output: Ensuring models adhere to strict formatting requirements

Bridging the Gap Between Real-World Media and AI Understanding I specialize in high-fidelity Multimodal Data Annotation, providing the precise human ground truth necessary for training advanced computer vision and speech recognition models. I transform unstructured video and audio into high-quality, structured datasets with a focus on temporal accuracy and semantic depth. My Core Expertise in Multimodal Labeling: Video Temporal Segmentation: Frame-by-frame action labeling and timestamping to define complex human behaviors and object interactions. Audio & Sentiment Annotation: Transcribing and labeling audio with metadata for tone, intent, sarcasm, and emotional nuance (Sentiment Analysis). Object Tracking & Bounding Boxes: Precise identification and persistent tracking of entities across dynamic video sequences. Multi-Modal Synchronization: Ensuring perfect alignment between visual cues and audio signals for seamless AI training. Edge Case Identification: Spotting visual artifac