Elisha Jackson

Key Skills

Software

Label Studio

CVAT

LabelImg

Labelbox

Playment

AWS SageMaker

Appen

Lionbridge

Top Subject Matter

Multimodal Annotation

LLM Output Evaluation

Audio & Speech Annotation

Top Data Types

Image

Text

Audio

Top Task Types

Classification

Bounding Box

Polygon

Entity Ner Classification

Point Key Point

Object Detection

Segmentation

RLHF

Transcription

Data Collection

Prompt Response Writing SFT

Text Summarization

Text Generation

Question Answering

Fine Tuning

Evaluation Rating

Freelancer Overview

Data Labelling Specialist. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Label Studio. Education includes Bachelor of Science, University of Uyo (2023). AI-training focus includes data types such as Image and Text and labeling workflows including Classification, Evaluation, and Rating.

Labeling Experience

Data Labelling Specialist — Atlas Capture

Label StudioImageClassification

As a Data Labelling Specialist for Atlas Capture, I completed over 1,000 annotation tasks on image, text, and structured datasets. I interpreted complex guidelines to resolve edge cases and collaborated on quality assurance reviews to improve labeling consistency. My focus was on generating reliable training data to support AI model development across computer vision and NLP workflows. • Annotation tasks included labeling images, classifying text, and tagging structured data. • Ensured productivity and high-quality outputs meeting project expectations. • Performed QA reviews reducing errors by 20%. • Streamlined guidelines and decision frameworks for better labeling accuracy.

As a Data Labelling Specialist for Atlas Capture, I completed over 1,000 annotation tasks on image, text, and structured datasets. I interpreted complex guidelines to resolve edge cases and collaborated on quality assurance reviews to improve labeling consistency. My focus was on generating reliable training data to support AI model development across computer vision and NLP workflows. • Annotation tasks included labeling images, classifying text, and tagging structured data. • Ensured productivity and high-quality outputs meeting project expectations. • Performed QA reviews reducing errors by 20%. • Streamlined guidelines and decision frameworks for better labeling accuracy.

2025 - Present

AI Evaluation — Outlier AI Subcontractor

Label StudioText

As an AI Evaluation Subcontractor for Outlier AI, I conducted entry-level LLM evaluation tasks by reviewing and scoring AI-generated model outputs. I ensured alignment with instructions and consistently maintained rigorous accuracy and quality standards. My work contributed to improving the reliability and alignment of AI models in natural language processing tasks. • Evaluated and rated over 600 large language model outputs. • Identified inconsistencies and flagged errors in AI responses. • Efficiently managed high-volume annotation and evaluation assignments. • Helped improve AI output quality for commercial NLP systems.

As an AI Evaluation Subcontractor for Outlier AI, I conducted entry-level LLM evaluation tasks by reviewing and scoring AI-generated model outputs. I ensured alignment with instructions and consistently maintained rigorous accuracy and quality standards. My work contributed to improving the reliability and alignment of AI models in natural language processing tasks. • Evaluated and rated over 600 large language model outputs. • Identified inconsistencies and flagged errors in AI responses. • Efficiently managed high-volume annotation and evaluation assignments. • Helped improve AI output quality for commercial NLP systems.

2024 - 2025

AI Audio Data Annotator

AudioTranscription

- Annotated and transcribed speech datasets used to train automatic speech recognition (ASR) models and voice-enabled AI systems. - Labeled audio clips with speaker segmentation, timestamps, and acoustic event markers such as background noise and overlapping speech. - Evaluated pronunciation patterns, speech clarity, and accent variations across diverse audio samples. - Maintained 95%+ annotation accuracy while processing over 1,200 audio annotation tasks. - Followed detailed project guidelines to ensure dataset consistency and reliability.

- Annotated and transcribed speech datasets used to train automatic speech recognition (ASR) models and voice-enabled AI systems. - Labeled audio clips with speaker segmentation, timestamps, and acoustic event markers such as background noise and overlapping speech. - Evaluated pronunciation patterns, speech clarity, and accent variations across diverse audio samples. - Maintained 95%+ annotation accuracy while processing over 1,200 audio annotation tasks. - Followed detailed project guidelines to ensure dataset consistency and reliability.

2023 - 2025

Education

Work History