Christopher James

Key Skills

Software

AWS SageMaker

Diffgram

Doccano

Labelbox

SuperAnnotate

Other

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Bounding Box

Classification

Entity Ner Classification

Question Answering

Translation Localization

Freelancer Overview

With over five years of hands-on experience labeling text, image, and audio data for AI systems, I specialize in producing high-accuracy annotations that power language models and computer vision systems. I’ve contributed to NLP and image-classification projects for enterprise-scale clients through Scale AI and Appen, maintaining over 98% accuracy across multilingual datasets. My expertise spans entity recognition, sentiment classification, safety review, and transcription QA, with deep familiarity in tools like Labelbox, SuperAnnotate, and Amazon SageMaker Ground Truth. I’m skilled at interpreting ambiguous data, refining labeling guidelines, and ensuring dataset consistency—making me a reliable partner for teams seeking precision and quality in AI training.

Labeling Experience

NLP Entity Tagging for Medical & Legal Documents

DiffgramTextEntity Ner ClassificationClassification

Labeled entity spans such as patient identifiers, dates, and medical terms for de-identification and model training. Helped enhance entity extraction accuracy in confidential documents by refining taxonomy definitions and performing inter-annotator calibration reviews.

Labeled entity spans such as patient identifiers, dates, and medical terms for de-identification and model training. Helped enhance entity extraction accuracy in confidential documents by refining taxonomy definitions and performing inter-annotator calibration reviews.

2024 - 2025

Image Classification & Object Detection

SuperannotateImageBounding BoxPolygon

Annotated thousands of image frames for object recognition models used in vehicle navigation and retail product categorization. Delivered consistent bounding boxes and polygon masks per pixel-level accuracy requirements.

Annotated thousands of image frames for object recognition models used in vehicle navigation and retail product categorization. Delivered consistent bounding boxes and polygon masks per pixel-level accuracy requirements.

2023 - 2025

LLM Evaluation & Safety QA

LabelboxTextClassificationEvaluation Rating

Evaluated and labeled thousands of text samples to improve LLM response safety, accuracy, and contextual coherence. Classified outputs by category, tone, and factual correctness. Collaborated with QA teams to maintain a 98%+ annotation accuracy rate and refine ambiguous guideline cases.

Evaluated and labeled thousands of text samples to improve LLM response safety, accuracy, and contextual coherence. Classified outputs by category, tone, and factual correctness. Collaborated with QA teams to maintain a 98%+ annotation accuracy rate and refine ambiguous guideline cases.

2023 - 2024

Search Intent Classification for ML Training

OtherTextRelationshipClassification

Classified and scored millions of user queries to train intent-recognition algorithms powering global search platforms. Conducted keyword taxonomy mapping and quality scoring to identify false positives and improve content retrieval models.

Classified and scored millions of user queries to train intent-recognition algorithms powering global search platforms. Conducted keyword taxonomy mapping and quality scoring to identify false positives and improve content retrieval models.

2022 - 2023

Education

Work History