Hala Khanjar

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Image

Medical Dicom

Text

Video

Top Task Types

Action Recognition

Audio Recording

Bounding Box

Classification

Data Collection

Diagnosis

Emotion Recognition

Entity Ner Classification

Evaluation Rating

Mapping

Object Detection

RLHF

Segmentation

Text Generation

Transcription

Translation Localization

Freelancer Overview

I am an experienced AI trainer and data annotator with a strong background in molecular biology, genetics, and scientific research. My work includes designing and applying structured rubrics for evaluating large language model outputs, annotating and correcting image-based Q&A datasets, and ensuring high-quality multimodal data for AI training. I am skilled at verifying factual accuracy, logical consistency, and instruction adherence, as well as providing detailed feedback to improve model performance. My technical expertise spans PCR, HPLC, ELISA, statistical analysis, and bioinformatics tools, which enables me to handle complex STEM and biomedical data with precision. I am adept at meeting productivity benchmarks while maintaining strict quality standards, and I have contributed to both AI and scientific projects that require meticulous attention to detail and clear documentation.

Labeling Experience

Image-Based Q&A Annotation and AI Evaluation — Outlier

Scale AIImageEntity Ner ClassificationSegmentation

Worked on an image-based data labeling and quality assurance project for training multimodal AI models on the Outlier platform. Reviewed and corrected pre-labeled image question-and-answer datasets. Verified annotation accuracy, extracted precise textual and visual information from images, and generated clear, standalone corrected responses. Evaluated AI outputs for factual accuracy, logical reasoning, linguistic quality, and scientific validity, especially in biology-related content. Applied strict quality control standards and followed detailed annotation guidelines. Maintained high productivity benchmarks while ensuring consistent annotation quality. Contributed high-quality human feedback to supervised fine-tuning (SFT) and reinforcement learning (RLHF) workflows.

Worked on an image-based data labeling and quality assurance project for training multimodal AI models on the Outlier platform. Reviewed and corrected pre-labeled image question-and-answer datasets. Verified annotation accuracy, extracted precise textual and visual information from images, and generated clear, standalone corrected responses. Evaluated AI outputs for factual accuracy, logical reasoning, linguistic quality, and scientific validity, especially in biology-related content. Applied strict quality control standards and followed detailed annotation guidelines. Maintained high productivity benchmarks while ensuring consistent annotation quality. Contributed high-quality human feedback to supervised fine-tuning (SFT) and reinforcement learning (RLHF) workflows.

2025

Education

Work History