Yusuf A.

Key Skills

Software

Don't disclose

Other

Data Annotation Tech

Toloka

Top Subject Matter

AI output benchmarking and structured quality assessment for analytical prompts

Financial dataset validation

anomaly detection

Top Data Types

Text

Image

Video

Audio

Document

Top Task Types

Data Collection

Classification

Entity (NER) Classification

RLHF

Bounding Box

Segmentation

Object Detection

Tracking

Freelancer Overview

AI Data Labeling & Evaluation Specialist | Outlier / Multimango. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Outlier, Multimango, and Data Annotation Tech. Education includes Bachelor of Science, Olabisi Onabanjo University (2025). AI-training focus includes data types such as Image, Video, and Audio and labeling workflows including Evaluation, Rating, and RLHF.

Labeling Experience

Freelance Data Analyst and AI Output Reviewer - Independent

TolokaImageClassificationEntity (NER) Classification

Freelance data analyst and AI output reviewer focused on creating structured, verifiable analytical questions and evaluating AI responses against defined criteria. You reviewed outputs for accuracy, reasoning completeness, and instruction-following quality across multiple prompt types and tasks. You also worked with structured JSON event data, validating results against expected schemas and documented edge cases. • Designed scoping for analytical tasks with objective, verifiable answers • Reviewed and rated AI-generated outputs for accuracy, reasoning completeness, and instruction-following • Parsed and cross-referenced structured JSON event data via WebSocket inspection • Validated outputs against expected schemas, including handling documented edge cases

2025 - Present

Freelance Data Analyst & AI Output Reviewer (Independent)

Don't discloseText

Performed structured evaluation of AI-generated outputs by rating accuracy, reasoning completeness, and instruction-following against predefined criteria. Designed verifiable analytical questions so responses could be assessed objectively for correctness rather than subjective interpretation. Captured and validated structured JSON event data by parsing cross-session values and checking schema edge cases. • Rated AI outputs for correctness across varied prompt types and analytical tasks. • Defined scoring dimensions covering accuracy, reasoning, and instruction adherence. • Validated outputs against expected JSON schema and documented edge cases. • Cross-referenced values across sessions using structured event data.

2025 - Present

AI Data Annotator | DataAnnotation.tech

Data Annotation TechTextRLHF

Annotated and evaluated AI-generated text for quality, accuracy, and policy compliance in an ongoing data labeling workflow. Conducted RLHF ranking by rating responses across helpfulness, harmlessness, and factual accuracy criteria. Flagged problematic outputs and delivered structured written feedback to improve model alignment and safety. • RLHF response ranking (helpfulness, harmlessness, factual accuracy) • Text quality and policy compliance evaluation • Identification of problematic outputs • Structured written feedback for alignment improvement

2023 - Present

AI Data Labeling & Evaluation Specialist | Outlier / Multimango

ImageRLHFBounding Box

Performed high-volume annotation and evaluation tasks across image, video, audio, and text for RLHF model training pipelines. Labeled spatial language referring expressions, verified bounding boxes and image entities, and assessed AI-generated outputs against structured rubrics and error taxonomies. Applied multi-criteria pairwise evaluation frameworks and produced written justifications to support preference and quality decisions. • Spatial Language Referring Expression annotation for complex scenes • Bounding box verification and entity tagging for image datasets • Error taxonomy evaluation (instruction following, factuality, symbols, plausibility, aesthetics) • Triple Balance Framework pairwise image evaluation plus audio naturalness/quality ratings

2023 - Present

Freelance Data Annotator | Toloka / Handshake AI

TolokaImage

Completed diverse microtask annotation projects for AI training datasets spanning image classification, content relevance rating, and text quality evaluation. Performed audio quality checking and content evaluation to assess dataset readiness and recording attributes. Ensured consistent results by applying task-specific guidelines and rubrics to maintain high accuracy. • Image classification micro-annotations • Content relevance and text quality evaluation • Audio quality checking for training datasets • Rubric-guideline driven accuracy maintenance

2022 - Present

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Education

Work History