For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

LoginCreate account
Invite to Job
F
Florence Obunga

Florence Obunga

AI Specialist - LLM Workflows

USA flagArlington, Usa
$20.00/hrEntry LevelCVATLabelboxLabel Studio

Key Skills

Software

CVATCVAT
LabelboxLabelbox
Label StudioLabel Studio
Scale AIScale AI
SuperAnnotateSuperAnnotate
V7 LabsV7 Labs

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor
AudioAudio
ImageImage
TextText

Top Task Types

Bounding BoxBounding Box
ClassificationClassification
Data CollectionData Collection
Entity (NER) ClassificationEntity (NER) Classification
Object DetectionObject Detection
Point/Key PointPoint/Key Point
SegmentationSegmentation

Freelancer Overview

I am a detail-oriented AI specialist with hands-on experience in data labeling, annotation, and AI training data workflows. My background includes annotating 3D LiDAR point cloud data for autonomous vehicle projects, generating and evaluating prompts for LLM outputs, and providing feedback to improve AI accuracy and tone. I’ve contributed to projects across computer vision and natural language processing domains, managed remote teams for large-scale data labeling, and have strong skills in Python scripting, dataset integration, and workflow optimization. My technical expertise, combined with a flexible and remote-ready work style, allows me to deliver high-quality, accurate training data for a variety of AI applications.

Entry LevelEnglish

Labeling Experience

AI Trainer & Data Annotation Specialist

ImagePolygon
Worked on large-scale AI training and data annotation projects across computer vision and natural language processing domains. Annotated image, video, text, audio, and 3D LiDAR datasets using techniques such as bounding boxes, polygons, segmentation, and classification to support machine learning models. Delivered over 15,000 high-quality labeled data points per sprint with 98% accuracy while adhering to strict annotation guidelines and quality assurance standards. Performed multi-pass validation and contributed to error reduction and dataset consistency. Additionally supported LLM training through prompt evaluation, response rating, and reinforcement learning from human feedback (RLHF), improving model accuracy, relevance, and safety. Collaborated with teams to refine annotation workflows and optimize data pipelines for scalable AI deployment.

Worked on large-scale AI training and data annotation projects across computer vision and natural language processing domains. Annotated image, video, text, audio, and 3D LiDAR datasets using techniques such as bounding boxes, polygons, segmentation, and classification to support machine learning models. Delivered over 15,000 high-quality labeled data points per sprint with 98% accuracy while adhering to strict annotation guidelines and quality assurance standards. Performed multi-pass validation and contributed to error reduction and dataset consistency. Additionally supported LLM training through prompt evaluation, response rating, and reinforcement learning from human feedback (RLHF), improving model accuracy, relevance, and safety. Collaborated with teams to refine annotation workflows and optimize data pipelines for scalable AI deployment.

2024 - Present

AI Data Evaluator | Search Quality Rater

TextClassification
Evaluated search engine results and AI-generated outputs for relevance, accuracy, usefulness, and alignment with user intent. Assessed text, images, and video content using structured rating guidelines to ensure high-quality and reliable results. Applied critical thinking and research skills to verify factual correctness and distinguish between high-value and low-quality content. Performed classification and evaluation tasks including question answering and content ranking to improve search and AI system performance. Maintained high levels of accuracy and consistency while meeting productivity benchmarks in a remote work environment. Contributed to improving large language model (LLM) outputs through reinforcement learning from human feedback (RLHF).

Evaluated search engine results and AI-generated outputs for relevance, accuracy, usefulness, and alignment with user intent. Assessed text, images, and video content using structured rating guidelines to ensure high-quality and reliable results. Applied critical thinking and research skills to verify factual correctness and distinguish between high-value and low-quality content. Performed classification and evaluation tasks including question answering and content ranking to improve search and AI system performance. Maintained high levels of accuracy and consistency while meeting productivity benchmarks in a remote work environment. Contributed to improving large language model (LLM) outputs through reinforcement learning from human feedback (RLHF).

2023 - Present

LiDAR Data Annotation Specialist | Autonomous Vehicles

3D SensorCuboid
Annotated and validated large-scale 3D LiDAR point cloud datasets for autonomous vehicle systems, labeling objects such as vehicles, pedestrians, road structures, and environmental features. Utilized cuboids, segmentation, and key point techniques to accurately represent spatial relationships in 3D environments. Processed and quality-checked over 10,000 datasets while maintaining 98% annotation accuracy and strict adherence to project guidelines. Applied multi-pass validation and quality assurance workflows to ensure consistency and precision across datasets. Contributed to improving machine learning model performance by identifying labeling inconsistencies, reducing error rates, and supporting the development of reliable perception systems for real-world autonomous applications.

Annotated and validated large-scale 3D LiDAR point cloud datasets for autonomous vehicle systems, labeling objects such as vehicles, pedestrians, road structures, and environmental features. Utilized cuboids, segmentation, and key point techniques to accurately represent spatial relationships in 3D environments. Processed and quality-checked over 10,000 datasets while maintaining 98% annotation accuracy and strict adherence to project guidelines. Applied multi-pass validation and quality assurance workflows to ensure consistency and precision across datasets. Contributed to improving machine learning model performance by identifying labeling inconsistencies, reducing error rates, and supporting the development of reliable perception systems for real-world autonomous applications.

2023 - 2024

Image Annotation Specialist

ImageBounding Box
Labeled and classified datasets for computer vision models Used bounding boxes and classification techniques Ensured dataset quality and compliance

Labeled and classified datasets for computer vision models Used bounding boxes and classification techniques Ensured dataset quality and compliance

2020 - 2021

Education

U

University of California

Bachelor's degree, Computer Sscience

Bachelor's degree
2016 - 2021

Work History

S

Scale Al

AI Trainer & Data Annotation Specialist

Los Angeles
2024 - Present
T

TELUS Digital AI Data Solutions

Search Quality Rater / AI Evaluator

Arlington
2023 - Present