Ignatius M.

Key Skills

Software

Scale AI

Appen

Remotasks

SuperAnnotate

Toloka

iMerit

Top Subject Matter

Autonomous Vehicles

Medical Imaging & Computer Vision

Computer Vision & NLP (Agriculture)

Top Data Types

Image

Video

Audio

Top Task Types

Bounding Box

Classification

Segmentation

Freelancer Overview

Results-driven Data Annotation Specialist with 5+ years of hands-on experience delivering high-quality training data for leading AI and machine learning organizations. Proficient in 2D/3D image and video annotation, bounding boxes, semantic segmentation, LiDAR point cloud labeling, and 3D object annotation. Combines deep technical knowledge with a genuine passion for artificial intelligence to consistently produce precise, scalable datasets that power real-world AI systems. Experienced in collaborating with global AI training platforms, adhering to strict quality benchmarks, and continuously improving annotation workflows. Annotation Types Image & Video Annotation, Bounding Boxes, Polygon Segmentation, Keypoint Labeling, Semantic Segmentation, Instance Segmentation 3D & LiDAR LiDAR Point Cloud Annotation, 3D Bounding Boxes, 3D Object Detection, Sensor Fusion Data Labeling ML & AI Machine Learning Fundamentals, Training Data Pipelines, Model Evaluation, Data Quality Assurance, AI Ethics & Bias Awareness Programming Python, JavaScript, R, MATLAB Tools & Platforms Scale AI, Appen, Labelbox, CVAT, Supervisely, V7 Darwin, Roboflow, CloudFactory Soft Skills Attention to Detail, High Throughput, Deadline Management, Remote Collaboration, Continuous Learning

Labeling Experience

Freelance Data Annotation Specialist

Scale AI3D SensorBounding Box

As a Freelance Data Annotation Specialist at Scale AI, I contributed to large-scale autonomous driving datasets by labeling LiDAR point clouds and 3D bounding boxes. I performed high-accuracy 2D image and video annotations using bounding boxes, polygons, and segmentation masks for computer vision projects. I collaborated with QA teams to review, validate, and improve annotation outputs, consistently maintaining high throughput and quality standards. • Labeled thousands of LiDAR frames and synchronized camera images for perception models • Utilized Scale AI's platform and annotation best practices • Specialized in object detection for autonomous vehicles • Achieved a 99%+ quality acceptance rate across image and video data

2022 - Present

Data Annotation Contractor

AppenImageClassification

As a Data Annotation Contractor with Appen, I executed image classification, object detection, and scene understanding tasks for major technology clients' AI pipelines. I specialized in video annotation projects, particularly frame-by-frame tracking and temporal consistency labeling. I annotated medical imaging data while adhering to strict HIPAA-compliant protocols. • Participated in annotator calibration sessions to standardize labeling • Used Appen platform and varying image/video labeling tools • Worked on healthcare and general object classification projects • Delivered annotations supporting model training at scale

2020 - 2021

Junior Data Annotator & ML Research Assistant

ImageSegmentation

As a Junior Data Annotator & ML Research Assistant, I assisted in building annotated datasets for research in computer vision and natural language processing. I developed preprocessing scripts and applied statistical analysis to evaluate data quality. I contributed annotated image datasets to a published research project on crop disease detection. • Used Python, R, and MATLAB for data annotation and evaluation • Contributed to machine learning-based agricultural applications • Focused on image segmentation and object annotation • Supported academic research at JKUAT

2019 - 2020

Education

Work History