Eliakim S.

Key Skills

Software

Scale AI

Top Subject Matter

Autonomous vehicles and LiDAR annotation for object detection

Ecological research (tree instance/species labeling) and building crack detection annotation

Top Data Types

3D Sensor

Top Task Types

Object Detection

Entity (NER) Classification

Classification

Freelancer Overview

Data Annotator Specialist at Scale AI. Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI. Education includes Bachelor of Science, Lyceum of the Philippines University Cavite (2026) and Bachelor of Science, Mapua Malayan Colleges Laguna (2024). AI-training focus includes data types such as 3D Sensor and labeling workflows including Object Detection, Entity (NER) Classification, and Classification.

Labeling Experience

Data Annotator Specialist at Scale AI

Scale AI3D SensorObject Detection

Served as a Data Annotator Specialist on the Proof of Concept (POC) team for autonomous-vehicle related LiDAR annotation. Produced annotated LiDAR data used for model training and validation workflows and supported demonstrations of annotation quality. Achieved 97%+ annotation accuracy for object detection and contributed to higher safety outcomes for self-driving systems. • Performed LiDAR annotation for autonomous vehicle projects • Delivered high-impact demos and trials showcasing LiDAR labeling • Reached 97%+ object detection annotation accuracy • Prepared high-quality LiDAR datasets to support multiple autonomous vehicle and delivery robot launches

2019 - 2024

Data Annotator Specialist at APEX Co., Ltd.

Scale AI3D SensorEntity (NER) ClassificationClassification

Worked as a Data Annotator Specialist at APEX Co., Ltd., performing large-scale instance annotation and classification tasks. Annotated and classified 47,000+ tree instances within a multi-month period to support ecological research and conservation efforts. Improved dataset diversity by labeling 180 tree species and contributed to building crack detection accuracy by annotating structural defects. • Annotated and classified 47,000+ tree instances • Labeled 180 distinct tree species to increase dataset diversity • Annotated building crack defects to improve detection accuracy • Supported ecological research, conservation, and structural risk identification goals

2023 - 2023

Education

Work History