Samuel S.

Key Skills

Software

Toloka

Other

Top Subject Matter

AI prompt and response evaluation

text dataset labeling

model output ranking for safety and usefulness.

Top Data Types

Text

Document

Top Task Types

Transcription

Freelancer Overview

AI Data Annotator & Prompt Evaluator (Human-in-the-Loop). Brings 2+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Toloka and Other. Education includes Master of Science, University of Kyrenia (2023) and Bachelor of Science, Kherson State Maritime Academy. AI-training focus includes data types such as Text and Document and labeling workflows including Evaluation, Rating, and Transcription.

Labeling Experience

Senior Administrative & Documentation Specialist

OtherDocumentTranscription

Performed exact word-for-word transcription of complex administrative documents, including multi-page Memorandums of Understanding (MOUs) related to public health collaborations. Conducted analysis and cross-checking to prepare formal itemized quotations, ensuring mathematical and structural accuracy prior to final reporting. Documented evidence-based reasons for layout and textual changes while ensuring all edits complied with project guidelines. • Transcribed dense legal/administrative text with 100% fidelity to originals. • Identified and rectified mathematical discrepancies in quotations valued at 1.5M+ Naira. • Audited structural integrity of digitized outputs before stakeholder delivery. • Communicated layout/text adjustment justifications in writing aligned to guidelines.

2025 - Present

AI Data Annotator & Prompt Evaluator (Human-in-the-Loop)

TolokaText

Evaluated and corrected AI-generated prompts and responses to improve logical consistency, grammar, and formatting for downstream model quality. Reviewed and labeled complex text datasets according to strict project guidelines to produce high-quality ground-truth data for machine learning training. Rated and ranked model outputs using relevance, helpfulness, and safety metrics to support the human-feedback loop for aligning AI behavior with user expectations. • Fixed logical flaws, grammatical errors, and formatting inconsistencies in prompt/response drafts. • Produced labeled examples for machine learning training cycles based on provided criteria. • Scored outputs across relevance/helpfulness/safety dimensions for evaluation-driven iteration. • Performed human-in-the-loop review activities as part of RLHF-style alignment workflows.

2025 - Present

Education

Work History