US-Based Remote AI Annotation Specialist & Data Labeling Expert
AI Annotation & Data Labeling Expertise from Ashley Rogers
Available for immediate remote contract work. Highly experienced in large-scale Video-Language Model (VLM) training, bounding box generation, 360° video labeling, Natural Language Processing (NLP) data analysis, audio transcription, and high-precision image annotation for generative AI model tasks.
My professional background includes processing thousands of high-precision AI annotations across diverse datasets. I possess a highly refined capacity for advanced pattern recognition, anomaly detection, verifying phonetic accuracy against somatic intent, and auditing data integrity. By anticipating friction points in complex RLHF (Reinforcement Learning from Human Feedback) workflows, I systematically catch critical edge cases, data corruptions, and semantic ambiguities that standard evaluation processes miss.
Availability
Part time or Full time (preferably 40+ hours). 20 to 80 hours weekly for 1 to 6+ month projects.
Task Completion Rate
600 easy tasks in approximately 8 hours. 60 complex tasks done in approximately 8 hours.
Annotation Components
Instruction Following
Prompted objects, subjects, environments, sounds, and actions must all be provided.
Visual Issues
Compressed texture
Flickering texture effect
Gradient banding
Framerate abnormalities
Blur
Overexposed lighting
Zoom
Camera framing
Scene cuts
Color saturation
Color saturation
Phonetic glitching
AI Generated
Morphed objects and anatomy
Extra limbs
Actions defying gravity
Unnatural textures
Unnatural lighting that doesn’t coincide with the environment
Unnatural or incorrect anatomy
Phasing through objects, subjects, or the environment
Audio Issues
Diarization correctness
Unsynchronized voice to mouth
Mispronunciation
Disrupted speech and audio
Synthetic voice effect
Pacing
False start
Digital artifacts (garbled speech, metallic tone, wobbling pitch, pops, clicks,
Compressed audio
Missing audio
Audio stuttering
Incorrect sounds
Video-Language Model (VLM) Annotation & Bounding Box Projects
Human Recorded Video Data QA Validation
Verified bounding boxes and masks for video content, ensuring spatial accuracy and model alignment. Conducted thorough Quality Assurance (QA) and factual data integrity validation of AI-generated annotations, identifying edge cases and functional conflict potential.
360° Video Generative AI Annotation
Worked as a Reviewer AI Trainer, annotating and reviewing 360° video data for AI training models. Focused on complex object tracking, anomaly detection, and scene understanding. Processed thousands of frames utilizing precise bounding box and masking techniques.
Audio Annotation, NLP Data Analysis & Transcription
Transcribed thousands of English audio files featuring complex East Asian voice accents using Labelbox. Applied NLP (Natural Language Processing) evaluation to verify phonemes against semantic intent, minimizing errors caused by conversational ambiguity and improving language app datasets.
Audio Recording & TTS Generative AI Training
Created high-quality audio recordings for AI-generated Text-to-Speech (TTS) scripts. Evaluated the accuracy of AI-generated data by comparing it against factual sources, and rated other users’ audio prompts for quality, somatic intent, and output consistency.
LLM Prompt Engineering & Image Data Labeling
Human Created & AI-Generated Media Annotation
Performed advanced data annotation and RLHF tasks for human and AI-generated media using Airtable, Parimango, Multimango, and Facebook’s internal annotation platform. Completed thousands of image annotation tasks to improve LLM and generative AI processing, clarity, and output effectiveness.
Project Leadership, Auditing & Process Improvement
Strategically analyzed and restructured existing project guidelines for a high-dollar video annotation project. Improved navigational interfaces and data formats to optimize user comprehension, mitigate instructional ambiguity, and greatly increase output efficiency.
Served as an authoritative Reviewer for 1 Alignerr audio AI training project and 4 micro1 projects (AI-generated video annotation and human-created audio tagging). Ensured strict data integrity, factual correctness, and QA standards were met while providing detailed feedback to optimize the future performance of annotation experts.
Ashley Rogers 