Ly Y.

Key Skills

Software

Data Annotation Tech

AWS SageMaker

Anno-Mage

Appen

Argilla

Axiom AI

Clickworker

CloudFactory

CrowdSource

CVAT

Dataloop

Datatroniq

Datumbox

Datasaur

Datature

Top Subject Matter

Computer vision image understanding and AIGC content quality control

3D perception and point cloud annotation

AIGC creative operations and prompt-driven content optimization

Top Data Types

Image

3D Sensor

Video

Top Task Types

Segmentation

Cuboid

Prompt + Response Writing (SFT)

Polyline

Point/Key Point

Entity (NER) Classification

Bounding Box

Classification

Object Detection

Question Answering

Text Generation

RLHF

Evaluation/Rating

Polygon

Text Summarization

Data Collection

Freelancer Overview

AI Data Annotator at Moxin (Hangzhou) Technology Co., Ltd. (Image semantic segmentation and quality labeling). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Arts, Changsha University (2022) and Bachelor of Arts, Peking University Opera Research Institute (2023). AI-training focus includes data types such as Image, 3D Sensor, and Video and labeling workflows including Segmentation, Cuboid, and Prompt + Response Writing (SFT).

Labeling Experience

AI Data Annotator at Moxin (Hangzhou) Technology Co., Ltd. (3D point cloud labeling)

3D SensorCuboid

Labeled 3D point cloud data to support object representation in algorithm training. Processed large volumes of point cloud frames and maintained annotation quality throughout the labeling cycle. Participated in validation activities to improve team consistency and labeling reliability. • Annotated 3D point cloud data across 3,000+ frames • Performed fine-grained classification on point cloud inputs • Participated in cross-validation and rule optimization • Supported algorithm optimization using labeled results

2026 - 2026

AI Data Annotator at Moxin (Hangzhou) Technology Co., Ltd. (Image semantic segmentation and quality labeling)

ImageSegmentation

Performed fine-grained image annotation and classification for indoor and outdoor scenes, delivering high accuracy above 95%. Supported label consistency through cross-validation and rule optimization. Contributed labeled outputs to downstream algorithm optimization and quality evaluation workflows. • Pixel-level semantic segmentation for 100–150 images per day • Image defect labeling for AIGC quality evaluation • Built and maintained a bad-case library to guide improvements • Conducted quality evaluation of AI-generated content outputs

2026 - 2026

AI Creative Operations (AIGC Track) at ByteDance (workflow construction and prompt/policy optimization)

VideoPrompt + Response Writing (SFT)

Constructed and iterated an AIGC automated short-video production workflow by generating and refining prompt/response artifacts and content structure for ad creative. Monitored CTR, completion rate, and other metrics to reverse-optimize prompts and the resulting video structure. Helped improve performance for a brand advertising series by 15% through data-driven content optimization. • Built end-to-end script → material → editing workflow • Reverse-optimized prompts and video structure from campaign metrics • Created SOPs and prompt library for consistent AI creative generation • Trained operations team on using the AI creative assistant

2024 - 2025

Education

Work History