Shannon Lewis

Completed distributed human-intelligence tasks covering data collection, annotation, evaluation, transcription, research, validation, classification, and content review in support of ML development and large-scale labeling needs.

Applied project-specific annotation guidelines to label and structure information for machine-learning and LLM workflows, including NER/NLP (people, places, organizations, events, dates), sentiment/intent/semantic labeling, and classification. Performed image annotation with bounding boxes and polygon segmentation, plus extraction and validation from scans, handwritten documents, audio, and video requiring human interpretation. Reviewed and ranked AI-generated outputs using preference-based judgment aligned with RLHF and reward-model development, focusing on contextual understanding, consistency, and best-fit classification. Supported audio/speech tasks such as transcription, speaker identification, emotion/intent/semantics labeling, and validation of low-quality inputs. Conducted structured data validation for accuracy, completeness, consistency, and relevance, and performed content moderation according to project rules.

Performed distributed human-intelligence tasks including data collection, annotation, evaluation, transcription, research, validation, classification, and content review to support machine-learning development. Supported large-scale review and labeling workflows, applying project-specific rules and quality requirements while resolving ambiguous, incomplete, or low-quality data using critical thinking and attention to detail.