Sylvester K.

Key Skills

Software

Don't disclose

Appen

Clickworker

Data Annotation Tech

Mercor

Micro1

Mindrift

OneForma

Telus

Top Subject Matter

Healthcare – Medical Imaging, Clinical Data,Applied Sciences & Life Sciences

Data Science – Data Analysis, Software Engineering, AI Evaluation & Analytics

Biomedical Engineering – Medical Imaging & Healthcare Technology

Top Data Types

Computer Code Programming

Medical Dicom

Text

Document

Image

Top Task Types

Data Collection

Evaluation/Rating

Question Answering

Classification

Function Calling

Transcription

RLHF

Text Generation

Object Detection

Cuboid

Polyline

Fine-tuning

Text Summarization

Point/Key Point

Entity (NER) Classification

Segmentation

Red Teaming

Computer Programming/Coding

Polygon

Prompt + Response Writing (SFT)

Bounding Box

Freelancer Overview

AI Evaluator and Software Quality Analyst | Mercor, Tester Work, test IO, and Meta AI. Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Mercor and Don't disclose. Education includes Bachelor of Science, Aligarh Muslim University (2023) and Bachelor of Science, Central University of Technology (2022). AI-training focus includes data types such as Text, Medical, and DICOM and labeling workflows including Evaluation, Rating, and Computer Programming.

Labeling Experience

Academic Peer Reviewer | Insights Into Imaging and Biomedical Signal Processing and Control

Don't discloseDocument

Performed academic peer review of radiology, medical imaging, and artificial intelligence manuscripts related to radiomics, deep learning, and clinical imaging applications. Critically evaluated research methodologies with emphasis on reproducibility, transparent reporting, and cross-validation validity. Provided detailed constructive feedback on study design, radiomics pipelines, and statistical bias to safeguard scientific quality for clinical audiences. • Methodology quality assessment for radiomics/deep learning studies • Reproducibility and reporting transparency evaluation • Cross-validation validity and statistical bias review • Constructive feedback to improve radiomics pipeline design

2026 - Present

Quantitative Imaging Researcher | Academic Research and Computational Imaging Collaboration

Don't disclose

Developed and implemented Python-based preprocessing pipelines for neuro oncology MRI analysis using MONAI, PyRadiomics, NumPy, and Pandas. Processed high-volume MRI scans from multi-institution datasets (e.g., BraTS 2021, BraTS 2023, BraTS Africa) to extract robust radiomic features for downstream modeling. Investigated and quantified how scanner heterogeneity and acquisition variability degrade predictive model performance across clinical sites. • Constructed MRI preprocessing workflows for radiomics analysis • Extracted radiomic features from multi-institution MRI datasets • Evaluated effects of scanner/acquisition variability on model performance • Prepared curated imaging-derived feature sets for research modeling

2025 - Present

AI Training and Model Evaluation Contributor

TextQuestion Answering

Making show that Ai responds to audio.voice notes as asked,,and transcribes the the correct words

2025 - Present

AI Training and Model Evaluation Contributor

Computer Code ProgrammingComputer Programming/Coding

Contributed to AI training and evaluation workflows through structured review, annotation, and quality assessment of model generated content. Evaluated responses for instruction adherence, logical consistency, clarity, factual reliability, and overall usefulness across diverse tasks and scenarios. Performed comparative analysis of outputs, identified recurring patterns and edge cases, documented observations, and supported continuous quality improvement processes. Worked with large volumes of structured information while maintaining accuracy, consistency, and attention to detail under established project guidelines. Experience included annotation activities, response evaluation, structured feedback generation, and supporting the development of reliable and user aligned AI outputs.

2025 - Present

AI Evaluator and Software Quality Analyst | Mercor, Tester Work, test IO, and Meta AI

MercorText

Evaluated frontier generative model outputs for accuracy, reasoning quality, safety, and compliance with project guidelines across multimodal text and voice tasks. Assessed platform functionality and usability by identifying algorithmic edge cases and delivering structured technical feedback to engineering teams. Maintained software quality assurance standards to optimize performance and reliability of advanced generative models. • Accuracy and reasoning quality scoring of model responses • Safety and compliance checks against project requirements • Edge-case identification with technical remediation feedback • Generative model quality assurance for multimodal tasks

2023 - Present

Education

Work History