Khalil Blackwell

Key Skills

Software

Labelbox

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Computer Code Programming

Top Label Types

Bounding Box

Object Detection

Tracking

Classification

Computer Programming Coding

Freelancer Overview

I am a detail-oriented AI Training Specialist with over three years of hands-on experience in video, image, and audio data annotation, as well as model evaluation. My background includes high-accuracy labeling for computer vision and speech-to-text projects, using tools like Labelbox, CVAT, Scale AI, Label Studio, and Amazon SageMaker Ground Truth. I have a strong track record in object detection, image segmentation, and audio transcription, ensuring data quality and integrity through rigorous quality assurance and validation processes. With a solid foundation in data cleaning, preprocessing, and dataset structuring, I am committed to supporting robust AI model training while maintaining strict confidentiality and data privacy standards. My technical skills include basic Python (Pandas, NumPy), Excel, and Google Sheets, which I leverage to optimize workflows and deliver reliable training data for machine learning applications.

Labeling Experience

AI Data Processing & Coding Support for Machine Learning

AppenComputer Code ProgrammingComputer Programming Coding

Worked on AI data processing and coding tasks to support machine learning model training and fine-tuning. Developed Python scripts to clean, preprocess, and structure large datasets including text, audio, and image data. Assisted in function calling and creating automated data pipelines to reduce repetitive tasks and improve workflow efficiency. Performed prompt engineering and response validation for natural language processing (NLP) models. Evaluated AI model outputs, identified errors, and documented improvements to optimize model performance. Supported supervised fine-tuning (SFT) tasks and reinforcement learning workflows by preparing high-quality, structured datasets. Collaborated with cross-functional teams to implement coding solutions for annotation workflows, ensuring datasets were accurate, consistent, and compliant with project guidelines. Maintained version control, documentation, and reproducibility for all projects, enabling smooth integration into AI training pipeline

Worked on AI data processing and coding tasks to support machine learning model training and fine-tuning. Developed Python scripts to clean, preprocess, and structure large datasets including text, audio, and image data. Assisted in function calling and creating automated data pipelines to reduce repetitive tasks and improve workflow efficiency. Performed prompt engineering and response validation for natural language processing (NLP) models. Evaluated AI model outputs, identified errors, and documented improvements to optimize model performance. Supported supervised fine-tuning (SFT) tasks and reinforcement learning workflows by preparing high-quality, structured datasets. Collaborated with cross-functional teams to implement coding solutions for annotation workflows, ensuring datasets were accurate, consistent, and compliant with project guidelines. Maintained version control, documentation, and reproducibility for all projects, enabling smooth integration into AI training pipeline

2023 - 2025

Autonomous Driving Video & Image Annotation

LabelboxVideoBounding BoxClassification

Participated in a large-scale autonomous driving AI project involving video and image data annotation for machine learning model training. Responsible for labeling and classifying thousands of frames, performing detailed object detection, segmentation, and tracking for vehicles, pedestrians, cyclists, traffic signs, and road infrastructure. Ensured temporal consistency across video sequences, identifying moving and stationary objects with high accuracy. Applied bounding boxes, semantic segmentation masks, and object classification to prepare data suitable for computer vision model training. Conducted rigorous quality assurance checks on all labeled data, collaborating with QA teams to resolve discrepancies and improve annotation standards. Maintained detailed records of labeling decisions, ensuring compliance with project guidelines and confidentiality requirements. Successfully annotated over 200,000 frames, improving dataset quality and contributing directly to AI model performance

Participated in a large-scale autonomous driving AI project involving video and image data annotation for machine learning model training. Responsible for labeling and classifying thousands of frames, performing detailed object detection, segmentation, and tracking for vehicles, pedestrians, cyclists, traffic signs, and road infrastructure. Ensured temporal consistency across video sequences, identifying moving and stationary objects with high accuracy. Applied bounding boxes, semantic segmentation masks, and object classification to prepare data suitable for computer vision model training. Conducted rigorous quality assurance checks on all labeled data, collaborating with QA teams to resolve discrepancies and improve annotation standards. Maintained detailed records of labeling decisions, ensuring compliance with project guidelines and confidentiality requirements. Successfully annotated over 200,000 frames, improving dataset quality and contributing directly to AI model performance

2021 - 2022

Autonomous Driving Video & Image Annotation Project

LabelboxImageBounding BoxObject Detection

Worked on a large-scale computer vision dataset for autonomous driving systems involving video and image annotation. Performed high-precision bounding box annotation, object detection, object tracking across frames, and semantic segmentation for vehicles, pedestrians, traffic signs, cyclists, and road infrastructure. Annotated over 200,000+ image frames and video sequences while maintaining strict quality standards and consistency guidelines. Conducted multi-level quality assurance reviews to ensure labeling accuracy above 98%. Collaborated with QA teams to resolve edge-case scenarios and improve annotation consistency for model training optimization.

Worked on a large-scale computer vision dataset for autonomous driving systems involving video and image annotation. Performed high-precision bounding box annotation, object detection, object tracking across frames, and semantic segmentation for vehicles, pedestrians, traffic signs, cyclists, and road infrastructure. Annotated over 200,000+ image frames and video sequences while maintaining strict quality standards and consistency guidelines. Conducted multi-level quality assurance reviews to ensure labeling accuracy above 98%. Collaborated with QA teams to resolve edge-case scenarios and improve annotation consistency for model training optimization.

2019 - 2021

Education

Work History