Jie Z.

Key Skills

Software

Label Studio

CVAT

Top Subject Matter

Signal demodulation

Modulation recognition

Deep learning signal analysis

Top Data Types

Audio

Image

Computer Code Programming

Top Task Types

Classification

Bounding Box

Freelancer Overview

AI Model Data Preparer and Signal Data Labeler for QPSK Deep Learning System. Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Master of Science, 西京学院 (2023) and Bachelor of Science, 温州理工学院 (2023). AI-training focus includes data types such as Audio and labeling workflows including Classification.

Labeling Experience

claude code ai训练

DocumentFine-tuning

利用cc 结合GitHub上流行skills，完成微信等主流软件桥接，并且进行主流项目学习

2025 - 2026

Experimental Data Labeler and Research Assistant for Deep Learning Signal Classification

AudioClassification

Reviewed and analyzed literature on modulation signal classification using machine learning/deep learning approaches. Designed experimental procedures comparing model parameters, data quality, and evaluation metrics via labeled datasets. Compiled clear labeling processes and outcomes for thesis and research publication support. • Managed the curation of audio signal datasets and labeled complex modulation types. • Coordinated model training and data evaluation cycles using labeled research datasets. • Synthesized findings for academic reporting and presentation utilizing annotation outcomes. • Supported reproducibility and analysis by maintaining detailed labeling documentation.

2023 - 2026

AI Model Data Preparer and Signal Data Labeler for QPSK Deep Learning System

AudioClassification

Contributed to designing a QPSK signal demodulation and recognition system utilizing deep learning models. Participated in tasks including data sample reading, normalization, label organization, and training/validation split using Python scripts. Assisted with feature extraction using VGG networks for signal classification and model evaluation across different signal-to-noise ratios. • Labeled and prepared large sets of modulated signal data for deep learning model training. • Structured and maintained organized training and validation datasets with clear, accurate annotations. • Analyzed mislabeling and data quality impact on classifier accuracy under multiple noise conditions. • Documented and summarized the experimental labeling process for software intellectual property and publication support.

2023 - 2026

Signal Data Annotator and Algorithm Validation Assistant for Smart City Sensing Project

AudioClassification

Conducted sample organization and metrics statistics for electronic information perception analysis in smart city scenarios. Used Python to process and label electronic signal data, supporting the project’s feasibility and evaluation. Contributed to developing technical documents and demonstrating labeled data outcomes for competition entry. • Aggregated, labeled, and validated signal perception datasets for smart city project modules. • Automated indicators and results comparison for labeled data quality assurance. • Prepared and presented labeling methodologies in project demonstrations and technical documents. • Collaborated in evidence-based workflow validation for competition submission.

2023 - 2024

Education

Work History