Neil S.

Key Skills

Software

Other

Label Studio

CVAT

Labelbox

Encord

Scale AI

Top Subject Matter

Multilingual text datasets for NLP/LLM training (e.g., medical, legal, technical)

Generalist computer vision labeling across specialized domains (e.g., medical, legal, technical)

LLM preference and safety evaluation datasets

Top Data Types

Text

Image

Document

Top Task Types

Entity (NER) Classification

Segmentation

RLHF

Freelancer Overview

Text annotation and AI training support (NLP/LLM evaluation, ranking, and QA). Core strengths include Other, Label Studio, and CVAT. AI-training focus includes data types such as Text and Image and labeling workflows including Entity (NER) Classification, Segmentation, and RLHF.

Labeling Experience

LLM training data support (RLHF ranking, red teaming, evaluation)

TextRLHF

Contributed to RLHF-style dataset creation and refinement by supporting preference ranking and prompt/response feedback workflows. Participated in model red teaming activities by generating and validating ranked outputs and evaluating responses for quality and safety. Applied data quality assurance to maintain reliable training signals. • Preference ranking data preparation (RLHF) • Prompt engineering feedback and response rating • LLM evaluation and red teaming support • Data quality assurance and auditing

Not specified

Computer vision annotation for model training (segmentation, boxes, keypoints)

Label StudioImageSegmentation

Carried out image annotation for tasks such as semantic segmentation and polygon-based labeling. Added structured annotations like keypoints and bounding boxes to support downstream computer vision model training. Ensured label consistency and quality control through guideline adherence and auditing. • Semantic segmentation and polygon annotation • Keypoints and bounding box labeling • Quality control & auditing for labeled datasets • Multitask image annotation using standard CV tools

Not specified

Text annotation and AI training support (NLP/LLM evaluation, ranking, and QA)

OtherTextEntity (NER) Classification

Performed AI training and labeling tasks across multiple text-oriented annotation types including NER, text classification, sentiment analysis, and summarization. Supported LLM evaluation and response ranking by applying quality checks against labeling guidelines and intended outputs. Used annotation platform workflows to ensure consistent, high-throughput dataset preparation for training and auditing. • NER (Named Entity Recognition) and entity labeling • Text classification and sentiment analysis • Summarization and preference-ranking (RLHF) data support • LLM evaluation, response ranking, and data quality assurance

Not specified