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Q
Qing Z.

Qing Z.

Contributing Writer (Outlier) — LLM & AI training / evaluation

Canada flagcoquitlam, Canada

Key Skills

Software

No software listed

Top Subject Matter

LLM evaluation
Rlhf Domain Expertise
and response quality labeling (bilingual Chinese-English)

Top Data Types

TextText
AudioAudio
ImageImage

Top Task Types

RLHFRLHF
Audio RecordingAudio Recording

Freelancer Overview

Contributing Writer (Outlier) — LLM & AI training / evaluation. Brings 16+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Diploma of Education, Western Community College (2023) and Diploma, Capilano University (2018). AI-training focus includes data types such as Text and Audio and labeling workflows including RLHF and Audio Recording.

Labeling Experience

Contributing Writer (Outlier) — TTS & audio-related AI training work

AudioAudioAudio RecordingAudio Recording

Contributed to TTS and Mandarin voice-related projects involving speech data preparation and quality checks for AI speech systems. Verified pronunciation accuracy, supported speech data collection, and performed localization review to ensure linguistic correctness and naturalness. Helped optimize conversational naturalness for speech-based models through iterative corrections and validation. • Pronunciation accuracy validation for Mandarin voice data • Speech data collection and localization review • Naturalness optimization for AI speech systems • Prompt/speech-model response refinement for improved conversational quality

2024 - Present

Contributing Writer (Outlier) — LLM & AI training / evaluation

TextTextRLHFRLHF

Worked on bilingual Chinese-English LLM and AI training evaluation tasks, primarily involving RLHF-related dataset labeling and reviewer alignment. Developed rubrics and evaluation criteria, then assessed model outputs for factuality, reasoning, safety, tone, and instruction adherence. Produced high-quality “golden responses” for benchmark calibration and improving consistency across review processes. • RLHF, response evaluation, and quality assurance for large language models • Rubric/evaluation-criteria development and output scoring • Golden-response creation for benchmark calibration and reviewer alignment • Multilingual scripts, linguistic correction, and response rewriting to improve conversational quality

2024 - Present

Education

W

Western Community College

Diploma of Education, Settlement Practicum

Diploma of Education
2023 - 2023
C

Capilano University

Diploma, Fine/Studio Arts

Diploma
2016 - 2018

Work History

S

South Vancouver Neighbourhood House

Settlement Practitioner

Vancouver
2023 - 2026
S

Sungiven Foods North America

Graphic Designer

Burnaby
2020 - 2022