Jeffrey Gordon Nadeau

Key Skills

Software

Appen

Clickworker

CVAT

Dataloop

Labelbox

Label Studio

OneForma

Roboflow

Scale AI

SuperAnnotate

Toloka

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Audio

Document

Image

Text

Video

Top Label Types

Action Recognition

Bounding Box

Classification

Data Collection

Evaluation Rating

Fine Tuning

Mapping

Object Detection

Point Key Point

Polygon

RLHF

Segmentation

Freelancer Overview

I have over seven years of experience working with high-volume data entry, content evaluation, and quality assurance, making me well-suited for data labeling and AI training data roles. My background includes reviewing and validating large datasets for accuracy, consistency, and compliance with detailed guidelines, as well as proofreading and editing both written and visual content. I am skilled in using tools such as Microsoft Office, Microsoft Dynamics, Zendesk, and Adobe Creative Suite to manage, annotate, and review data. My work has consistently required strong attention to detail, error detection, and the ability to follow complex instructions—skills that directly align with data annotation, response evaluation, and quality control tasks in AI model training. I am comfortable handling multiple projects simultaneously and thrive in environments where accuracy and clarity are critical to success.

Labeling Experience

AI Trainer

ClickworkerAudioBounding BoxPoint Key Point

open each audio clip in an annotation tool, review the labeling guidelines, listen through the clip, and apply the correct labels either to the whole clip or to specific time ranges by marking accurate start/end timestamps; you’d handle overlaps and unclear cases according to the rules (e.g., multiple labels or an “unknown” tag), add any required metadata like speaker IDs or noise/quality notes, then do a quick quality check by re-listening around boundaries and finally save/export the annotations in the required format (CSV/JSON/etc.).

open each audio clip in an annotation tool, review the labeling guidelines, listen through the clip, and apply the correct labels either to the whole clip or to specific time ranges by marking accurate start/end timestamps; you’d handle overlaps and unclear cases according to the rules (e.g., multiple labels or an “unknown” tag), add any required metadata like speaker IDs or noise/quality notes, then do a quick quality check by re-listening around boundaries and finally save/export the annotations in the required format (CSV/JSON/etc.).

2022 - 2025

AI Trainer

AppenImageBounding BoxPoint Key Point

reviewing images and applying structured labels, tags, or bounding markers to visual elements based on detailed guidelines. Tasks may include identifying objects, attributes, text, or relationships within images to create accurate, high-quality datasets used for training and evaluating computer vision models. The work requires strong attention to detail, consistency, and strict adherence to annotation rules to ensure data reliability.

reviewing images and applying structured labels, tags, or bounding markers to visual elements based on detailed guidelines. Tasks may include identifying objects, attributes, text, or relationships within images to create accurate, high-quality datasets used for training and evaluating computer vision models. The work requires strong attention to detail, consistency, and strict adherence to annotation rules to ensure data reliability.

2016 - 2022

Data Annotations

TolokaVideoBounding BoxSegmentation

reviewing video clips and applying structured labels, timestamps, or metadata to specific visual or audio elements based on detailed guidelines. Tasks may include identifying objects, actions, gestures, speech, scene changes, or behavioral patterns frame-by-frame to ensure accurate and consistent data for training and evaluating AI models. High attention to detail, guideline adherence, and quality control are essential to maintain dataset reliability.

reviewing video clips and applying structured labels, timestamps, or metadata to specific visual or audio elements based on detailed guidelines. Tasks may include identifying objects, actions, gestures, speech, scene changes, or behavioral patterns frame-by-frame to ensure accurate and consistent data for training and evaluating AI models. High attention to detail, guideline adherence, and quality control are essential to maintain dataset reliability.

2015 - 2017

Education

Work History