Lawrence Otomu

Key Skills

Software

Mindrift

Scale AI

Toloka

Remotasks

Top Subject Matter

3D Self driving cars imager

LLM Evaluation in English

Image annotation

Top Data Types

3D Sensor

Geospatial Tiled Imagery

Text

Top Task Types

Bounding Box

Evaluation/Rating

Prompt + Response Writing (SFT)

Text Generation

Freelancer Overview

I have extensive experience in data labeling and AI training data, specializing in tasks that require a high level of accuracy and attention to detail. My work has involved complex 3D data annotation projects, where I was responsible for labeling point clouds, segmenting scenes, and ensuring the precision of data used in AI models for applications such as autonomous driving and augmented reality. My ability to follow strict guidelines and maintain consistency across large datasets has been instrumental in delivering high-quality outputs that meet industry standards. What sets me apart is my strong analytical skills, combined with a deep understanding of machine learning requirements and data accuracy. I am proficient in using various annotation tools and have a proven track record of working on projects with tight deadlines and quality benchmarks. My commitment to continuous improvement and feedback integration ensures that I not only meet but exceed project expectations, contributing to the development of reliable and effective AI systems.

Labeling Experience

Bee Lidar

Scale AI3D SensorCuboid

The project involved annotating 3D data to create high-quality training datasets for machine learning models used in various applications such as autonomous driving, augmented reality, or robotics. The primary objective was to label different objects accurately within 3D space to enhance the precision of AI-driven models. Segmentation of 3D scenes to delineate between different objects or structures within a given environment. The project covered a substantial dataset, encompassing thousands to millions of 3D data points or frames. The scope included annotating multiple scenes or sequences, each containing complex 3D data requiring meticulous attention to detail.

The project involved annotating 3D data to create high-quality training datasets for machine learning models used in various applications such as autonomous driving, augmented reality, or robotics. The primary objective was to label different objects accurately within 3D space to enhance the precision of AI-driven models. Segmentation of 3D scenes to delineate between different objects or structures within a given environment. The project covered a substantial dataset, encompassing thousands to millions of 3D data points or frames. The scope included annotating multiple scenes or sequences, each containing complex 3D data requiring meticulous attention to detail.

2019 - 2022

Education

Work History