Robert Ndiritu

Key Skills

Software

Appen

Clickworker

Remotasks

Toloka

Scale AI

Other

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Video

Top Task Types

Audio Recording

Bounding Box

Data Collection

Emotion Recognition

Mapping

Freelancer Overview

I am an experienced AI Training and Data Labeling Specialist with hands-on experience supporting machine learning models through high-quality data annotation, validation, and quality assurance. I have worked on projects involving text, image, and audio data, including tasks such as classification, tagging, transcription, sentiment analysis, and bounding box annotation. I consistently follow detailed labeling guidelines and maintain high accuracy standards to ensure clean, reliable training datasets for AI systems. What sets me apart is my strong attention to detail, ability to work with large datasets under tight deadlines, and adaptability across different AI use cases and industries. I am comfortable using a wide range of annotation tools and platforms and thrive in remote, task-driven environments. My background demonstrates reliability, data integrity, and a solid understanding of how labeled data directly impacts model performance.

Labeling Experience

Image annotation

Scale AIImageBounding BoxClassification

Worked on a large-scale computer vision project focused on training AI models for autonomous driving systems. Responsibilities included precise annotation of road scene images using bounding boxes, polygons, and segmentation techniques to label vehicles, pedestrians, cyclists, traffic signs, lane markings, and traffic lights. Followed strict labeling guidelines to ensure high accuracy and consistency across thousands of frames captured in diverse weather, lighting, and traffic conditions. Performed quality assurance checks, corrected annotation errors, and collaborated with reviewers to meet project accuracy benchmarks. This work directly contributed to improving object detection, scene understanding, and real-time decision-making capabilities of self-driving vehicle models

Worked on a large-scale computer vision project focused on training AI models for autonomous driving systems. Responsibilities included precise annotation of road scene images using bounding boxes, polygons, and segmentation techniques to label vehicles, pedestrians, cyclists, traffic signs, lane markings, and traffic lights. Followed strict labeling guidelines to ensure high accuracy and consistency across thousands of frames captured in diverse weather, lighting, and traffic conditions. Performed quality assurance checks, corrected annotation errors, and collaborated with reviewers to meet project accuracy benchmarks. This work directly contributed to improving object detection, scene understanding, and real-time decision-making capabilities of self-driving vehicle models

2023 - 2023

Audio Transcription

OtherAudioText Generation

The project focused on converting large volumes of raw audio recordings into accurate, time-aligned text for use in speech recognition and voice-enabled AI systems. The scope included verbatim transcription of diverse audio types such as interviews, call center recordings, podcasts, and conversational speech in varying accents and noise conditions. Tasks involved speaker identification, timestamping, noise and filler-word tagging, and strict adherence to formatting and linguistic guidelines. Quality assurance was a core component of the scope, requiring error detection, proofreading, and validation against accuracy benchmarks to ensure clean, reliable training data for AI and machine learning models.

The project focused on converting large volumes of raw audio recordings into accurate, time-aligned text for use in speech recognition and voice-enabled AI systems. The scope included verbatim transcription of diverse audio types such as interviews, call center recordings, podcasts, and conversational speech in varying accents and noise conditions. Tasks involved speaker identification, timestamping, noise and filler-word tagging, and strict adherence to formatting and linguistic guidelines. Quality assurance was a core component of the scope, requiring error detection, proofreading, and validation against accuracy benchmarks to ensure clean, reliable training data for AI and machine learning models.

2023

Education

Work History