Daniel Njoroge

Key Skills

Software

Appen

Clickworker

Data Annotation Tech

Labelbox

Mindrift

Remotasks

Toloka

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Image

Top Task Types

Audio Recording

Classification

Data Collection

Prompt Response Writing SFT

Question Answering

Freelancer Overview

My core experience lies in transforming raw, unstructured data into high-quality, structured training datasets for machine learning models. This involves a deep understanding of annotation guidelines, consistency, and the critical impact of data quality on model performance. I am highly proficient in a range of tasks including bounding box and polygon annotation for object detection, semantic segmentation, text classification, sentiment analysis, and named entity recognition (NER). My key skill is a meticulous attention to detail and a rigorous quality assurance process, ensuring that labeled data is not only accurate but also consistent across large-scale projects, which is fundamental for training robust and unbiased AI. What sets me apart is my ability to act as a strategic partner in the AI development lifecycle, not just an annotator. I have contributed to projects in autonomous vehicle perception (labeling LiDAR and camera data for obstacle detection) and large language model (LLM) fine-tuning, where I crafted and refined instructional prompts and responses. My qualifications include a strong analytical mindset to identify edge cases and ambiguities in data, proactively suggesting improvements to labeling taxonomies and workflows to enhance both efficiency and dataset integrity. This combination of hands-on technical execution and a big-picture understanding of ML objectives allows me to deliver training data that truly elevates model accuracy and generalization.

Labeling Experience

Scale AI

Scale AIImageClassification

This project aims to build a high-quality dataset to train a machine learning model that automatically categorizes product images for a large e-commerce platform. The model will help improve search relevance and product recommendation systems by accurately identifying the primary category of an item from its image alone.

This project aims to build a high-quality dataset to train a machine learning model that automatically categorizes product images for a large e-commerce platform. The model will help improve search relevance and product recommendation systems by accurately identifying the primary category of an item from its image alone.

2018 - 2021

Education

Work History