Morris Njenga

Key Skills

Software

Appen

Dataturk

Diffgram

HiveMind

Prodigy

Roboflow

Scale AI

V7 Labs

Snorkel AI

Top Subject Matter

Autonomous Vehicle Imagery Annotation" Experienced in labeling and segmenting images for self-driving car applications, including object detection, lane recognition, and obstacle classification to enhance autonomous navigation systems.

Natural Language Processing (NLP) for Multilingual LLMs" Proficient in evaluating and fine-tuning large language models (LLMs), including text classification, entity recognition, and conversational AI systems, especially for multilingual support.

Satellite and Aerial Image Classification for Environmental Monitoring" Skilled in geospatial analysis for environmental conservation, including land-use classification, habitat mapping, and disaster impact assessment through satellite and aerial image data labeling

Top Data Types

Audio

Text

Video

Top Task Types

Bounding Box

Polygon

Polyline

Segmentation

Text Generation

Freelancer Overview

Data Labeling and AI Training Data Specialist with a strong foundation in computer vision, NLP, and geospatial analysis. Over five years of experience in annotating and labeling diverse data types—ranging from high-resolution satellite images for environmental monitoring to complex imagery for autonomous vehicles. Skilled in applying advanced labeling techniques, including bounding boxes, semantic segmentation, and 3D cuboids, for use in real-time AI applications across industries such as autonomous driving, environmental conservation, and natural language processing. Known for precision and quality in annotation, I have contributed to high-impact projects like improving LLM conversational models with multilingual data, detecting land-use changes for sustainable planning, and enhancing obstacle detection models for self-driving cars. Leveraging expertise in tools like Labelbox, CVAT, and Amazon SageMaker Ground Truth, I ensure high accuracy in data labeling that translates to superior model performance. Passionate about collaborating across teams to provide actionable insights and advance AI capabilities in dynamic, real-world settings.

Labeling Experience

Autonomous Vehicle Object Detection and Environmental Mapping

Snorkel AIVideoBounding BoxPolygon

This project focused on labeling video frames and images for the development of object detection models in autonomous vehicles. Tasks included annotating road scenes to identify and segment objects such as pedestrians, other vehicles, traffic signs, road lanes, and obstacles in diverse environments. The goal was to enhance the vehicle's perception system, enabling real-time decision-making for safe navigation. Labeling involved creating bounding boxes for object identification, polygon annotations for complex object segmentation, and semantic segmentation to map road features accurately. The project adhered to stringent quality control standards, ensuring that all annotations were 99% accurate, using double-checking and validation workflows for consistency across the dataset. The project size consisted of over 50,000 labeled video frames and images collected from urban, rural, and highway driving environments.

This project focused on labeling video frames and images for the development of object detection models in autonomous vehicles. Tasks included annotating road scenes to identify and segment objects such as pedestrians, other vehicles, traffic signs, road lanes, and obstacles in diverse environments. The goal was to enhance the vehicle's perception system, enabling real-time decision-making for safe navigation. Labeling involved creating bounding boxes for object identification, polygon annotations for complex object segmentation, and semantic segmentation to map road features accurately. The project adhered to stringent quality control standards, ensuring that all annotations were 99% accurate, using double-checking and validation workflows for consistency across the dataset. The project size consisted of over 50,000 labeled video frames and images collected from urban, rural, and highway driving environments.

2019 - 2023

Education

Work History