Moses M.

Key Skills

Software

Remotasks

Other

Top Subject Matter

AI training datasets via 3D asset annotation/creation (geometry and topology quality)

AI tutoring/training via evaluation and feedback on 3D creative tasks

Legal Services & Contract Review

Top Data Types

3D Sensor

Image

Text

Document

Top Task Types

Bounding Box

Polygon

Segmentation

Classification

Point/Key Point

Entity (NER) Classification

Polyline

Object Detection

Text Generation

Question Answering

Transcription

Data Collection

Computer Programming/Coding

Freelancer Overview

3D Modeling Specialist / AI Trainer — Remotask (Scale AI). Core strengths include Remotasks and Other. Education includes Diploma in Building Technology, Rift Valley Institute of Science and Technology (2023) and Kenya Certificate of Secondary Education (KCSE), Butula Boys High School (2018). AI-training focus includes data types such as 3D Sensor and labeling workflows including Evaluation and Rating.

Labeling Experience

Freelance AI Trainer – 3D & Creative Tasks — Outlier AI

Other3D Sensor

Contributed to AI model improvement on Outlier AI by completing high-quality 3D modeling and creative tasks intended to increase accuracy and output quality. Evaluated AI-generated 3D content and provided structured feedback based on modeling principles, geometry, and spatial correctness. Communicated process and problem-solving steps to support traceable and iterative remote training workflows. • Provided structured feedback on AI-generated 3D content and applied spatial/modeling accuracy checks. • Participated in remote, iterative pipelines while meeting deadlines and quality benchmarks across multiple task categories. • Collaborated within platform feedback systems to support continuous quality improvement. • Documented reasoning clearly and systematically to make review outcomes understandable and actionable.

2024 - Present

3D Modeling Specialist / AI Trainer — Remotask (Scale AI)

Remotasks3D Sensor

Worked on AI training tasks within Remotask by producing high-fidelity 3D assets from 2D reference imagery while aligning outputs to platform quality review guidelines. Completed structured 3D modeling deliverables across hard-surface, organic, and environment/architecture categories to support downstream model training. Iterated models based on feedback to improve geometry, proportions, topology, and volumetric fidelity for consistent dataset quality. • Created production-ready 3D models from 2D references, resolving occlusions and incomplete imagery with supplementary sources. • Produced hard surface assets such as vehicles, props, and machinery with clean topology for training pipelines. • Modeled organic forms (humans, animals, natural elements) emphasizing anatomical accuracy and organic structure. • Modeled environment and architectural scenes (buildings, landscapes, set pieces) for AI training datasets.

2024 - Present

Education

Work History