Key Skills

Software

Appen

Clickworker

CloudFactory

CVAT

Data Annotation Tech

OneForma

Remotasks

Toloka

Scale AI

Top Subject Matter

Machine Learning (ML) and Artificial Intelligence (AI)

Computer Vision and Data Labeling

Data Annotation and Machine Learning Models and Video Annotation

Top Data Types

3D Sensor

Computer Code Programming

Text

Top Task Types

Bounding Box

Computer Programming Coding

Cuboid

Data Collection

Prompt Response Writing SFT

Freelancer Overview

I am an accomplished professional in data labeling and AI training data development, with over 8 years of experience in annotating large-scale datasets for machine learning tasks such as object detection, image classification, and video segmentation. I have extensive hands-on experience with industry-standard annotation tools like Labelbox, CVAT, RectLabel and Scale AI to make sure that the data is highly qualitative, accurate, and supports the development of advanced AI models. I have managed a team, put in place strong quality control processes, and worked extensively with machine learning engineers and data scientists to help fine-tune their datasets for model training and evaluation. I have led projects in computer vision covering facial recognition, autonomous vehicles, and AI-enhanced healthcare systems. I have experience handling large datasets, validating data, and solving tough annotation challenges. With a strong foundation in machine learning and computer vision, along with a PhD in Computer Science, I have brought both technical expertise and collaboration to every project. My ability to mentor junior annotators and effectively communicate even the most complex concepts has allowed me to drive efficiency and ensure high-quality annotation practices across multiple projects.

Labeling Experience

Data Annotation Specialist ML or AI Training Data

Scale AIVideoBounding BoxCuboid

Project Description: Autonomous Vehicle Data Annotation for Navigation Systems Scope. This project focused on annotating data to improve object detection and decision-making for autonomous vehicles. The task involved annotating road scenarios from cameras and LIDAR sensors to help the system navigate complex environments. 1. Data Labeling Tasks - Object Detection : Labeling vehicles, pedestrians, and obstacles. - emantic Segmentation : Annotating road boundaries, lanes, and signals. - Depth Perception : Identifying object distances. - Vehicle Movement : Tagging dynamic movement patterns. 2. Project Size Annotated 300,000+ frames from 50+ hours of footage, covering 500 driving scenarios. A team of 20 annotators worked for 5 months. 3. Quality Measures Weekly accuracy reviews, cross-functional validation with engineers, continuous feedback loops, and regular audits to ensure high data quality.

Project Description: Autonomous Vehicle Data Annotation for Navigation Systems Scope. This project focused on annotating data to improve object detection and decision-making for autonomous vehicles. The task involved annotating road scenarios from cameras and LIDAR sensors to help the system navigate complex environments. 1. Data Labeling Tasks - Object Detection : Labeling vehicles, pedestrians, and obstacles. - emantic Segmentation : Annotating road boundaries, lanes, and signals. - Depth Perception : Identifying object distances. - Vehicle Movement : Tagging dynamic movement patterns. 2. Project Size Annotated 300,000+ frames from 50+ hours of footage, covering 500 driving scenarios. A team of 20 annotators worked for 5 months. 3. Quality Measures Weekly accuracy reviews, cross-functional validation with engineers, continuous feedback loops, and regular audits to ensure high data quality.

2018

Software Developers and Coders

Scale AIComputer Code ProgrammingComputer Programming Coding

Project Description: Software Development & Coding Data Annotation Scope: Annotated code datasets to train AI models for code prediction, bug detection, and automation in software development. Data Labeling Tasks: - Labeled functions, variables, and classes. - Tagged potential bugs and errors. - Categorized code snippets (e.g., sorting, searching). - Annotated code comments and documentation. Project Size: - Annotated 100,000+ lines of code. - 15 annotators over 4 months. Quality Measures: - Peer reviews, continuous validation, and integration testing for accuracy.

Project Description: Software Development & Coding Data Annotation Scope: Annotated code datasets to train AI models for code prediction, bug detection, and automation in software development. Data Labeling Tasks: - Labeled functions, variables, and classes. - Tagged potential bugs and errors. - Categorized code snippets (e.g., sorting, searching). - Annotated code comments and documentation. Project Size: - Annotated 100,000+ lines of code. - 15 annotators over 4 months. Quality Measures: - Peer reviews, continuous validation, and integration testing for accuracy.

2020 - 2024

Education

Work History