Jakub Rudnicki

Key Skills

Software

Telus

Lionbridge

Labelbox

Other

Top Subject Matter

Speech Recognition

AI Image Processing

AI Text Processing

Top Data Types

Audio

Image

Text

Document

Top Task Types

Audio Recording

RLHF

Evaluation/Rating

Freelancer Overview

Native Polish speaker with nearly a year of hands-on experience in AI training projects (RLHF), including speech, text, and image annotation for platforms such as Telus International, Lionbridge, and Prolific. I specialize in evaluating AI-generated content for factual accuracy, linguistic consistency, and instruction-following. I am highly proficient in working with complex annotation guidelines and participating in rigorous QA feedback loops to ensure high-quality training datasets. My unique value lies in combining technical literacy (ISTQB Foundation Level) with a strong analytical background. Since 2013, I have served as an Archivist at a District Court, managing the full lifecycle of sensitive, high-volume data sets, which has instilled in me exceptional precision and attention to detail. As a Master of Archival Science and a published author in the peer-reviewed journal "Archeion" (listed by the Ministry of Science), I bring expert-level research skills and advanced information analysis capabilities to every AI project.

Labeling Experience

AI Data Annotation & Research Contributor – Prolific (Text)

Text

This position focused on textual data annotation and evaluation to support AI training initiatives. Outputs generated by AI models were closely compared and assessed for factual accuracy, instruction adherence, and linguistic consistency. Recommendations for AI improvement were provided through structured feedback mechanisms. • Conducted comparative assessment of multiple AI-generated text outputs. • Identified linguistic inconsistencies and factual inaccuracies in AI responses. • Delivered structured feedback as per study-specific guidelines. • Supported ongoing research into AI and human interaction.

This position focused on textual data annotation and evaluation to support AI training initiatives. Outputs generated by AI models were closely compared and assessed for factual accuracy, instruction adherence, and linguistic consistency. Recommendations for AI improvement were provided through structured feedback mechanisms. • Conducted comparative assessment of multiple AI-generated text outputs. • Identified linguistic inconsistencies and factual inaccuracies in AI responses. • Delivered structured feedback as per study-specific guidelines. • Supported ongoing research into AI and human interaction.

2026 - Present

AI Data Annotation & Research Contributor – Prolific (Image)

Image

The primary responsibility was to annotate and evaluate image datasets for improving AI systems. Work also included comparison of AI-generated outputs and identifying key differences to suggest targeted model improvements. Structured feedback was given based on established study guidelines across diverse machine learning projects. • Provided detailed evaluations and ratings for labeled data. • Worked closely with research teams to refine annotation protocols. • Ensured high standards of data accuracy and compliance. • Participated in ongoing research related to AI and user behavior.

The primary responsibility was to annotate and evaluate image datasets for improving AI systems. Work also included comparison of AI-generated outputs and identifying key differences to suggest targeted model improvements. Structured feedback was given based on established study guidelines across diverse machine learning projects. • Provided detailed evaluations and ratings for labeled data. • Worked closely with research teams to refine annotation protocols. • Ensured high standards of data accuracy and compliance. • Participated in ongoing research related to AI and user behavior.

2026 - Present

AI Data Contributor – Audio & Speech Projects (Freelance / NDA)

TelusAudioAudio Recording

This role involved recording and delivering high-quality Polish speech datasets to support AI model training. Consistency in pronunciation, pacing, and tone was carefully maintained throughout the dataset to ensure data quality. Tasks were performed under strict technical requirements and deadlines, following detailed guidelines for annotation and recording. • Participated in QA feedback loops and conducted re-recording cycles. • Maintained a controlled recording environment, focusing on noise reduction and echo minimization. • Completed assignments using multiple platforms in compliance with NDA requirements. • Collaborated with international teams for dataset alignment and quality assurance.

This role involved recording and delivering high-quality Polish speech datasets to support AI model training. Consistency in pronunciation, pacing, and tone was carefully maintained throughout the dataset to ensure data quality. Tasks were performed under strict technical requirements and deadlines, following detailed guidelines for annotation and recording. • Participated in QA feedback loops and conducted re-recording cycles. • Maintained a controlled recording environment, focusing on noise reduction and echo minimization. • Completed assignments using multiple platforms in compliance with NDA requirements. • Collaborated with international teams for dataset alignment and quality assurance.

2025 - Present

Education

Work History