Maria Gil

Key Skills

Software

AWS SageMaker

Axiom AI

Clickworker

CloudFactory

CrowdSource

CVAT

Data Annotation Tech

Dataloop

Datasaur

Deep Systems

Figure Eight

Google Cloud Vertex AI

Humanatic

Img Lab

Labelbox

Label Studio

LightTag

Lionbridge

Mighty AI

Mindrift

OneForma

OpenCV AI Kit (OAK)

Redbrick AI

Scale AI

Snorkel AI

Surge AI

Toloka

Telus

V7 Labs

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Video

Top Task Types

Classification

Computer Programming Coding

Data Collection

Function Calling

Mapping

Freelancer Overview

I am an experienced data annotator and AI training specialist with a strong background in labeling images, videos, and text for machine learning applications. Over the years, I have contributed to projects across autonomous vehicles, e-commerce, and multimedia, providing high-quality annotations that enhance model accuracy and performance. My expertise includes object detection, segmentation, classification, and NLP data labeling, ensuring datasets meet rigorous standards for AI training. I am proficient in a variety of data labeling tools such as Labelbox, CVAT, and SuperAnnotate, and I am comfortable working with both structured and unstructured data. Known for my attention to detail, consistency, and efficiency, I have successfully completed large-scale annotation projects while maintaining high-quality standards. My goal is to contribute to AI initiatives by delivering precise, reliable, and well-documented training data.

Labeling Experience

Multimedia Data Annotation Specialist for AI Training

Scale AITextBounding BoxEntity Ner Classification

Performed large-scale annotation projects across images, videos, text, and audio for AI and machine learning applications. Tasks included object detection, segmentation, tracking, entity tagging, text summarization, emotion recognition, and audio labeling. Managed datasets ranging from 50,000 to 200,000+ samples, ensuring high-quality annotations that improved AI model accuracy. Applied strict quality control measures, maintained consistency, and followed detailed annotation guidelines to deliver reliable and actionable training data.

Performed large-scale annotation projects across images, videos, text, and audio for AI and machine learning applications. Tasks included object detection, segmentation, tracking, entity tagging, text summarization, emotion recognition, and audio labeling. Managed datasets ranging from 50,000 to 200,000+ samples, ensuring high-quality annotations that improved AI model accuracy. Applied strict quality control measures, maintained consistency, and followed detailed annotation guidelines to deliver reliable and actionable training data.

2024

Autonomous Vehicle Image & Video Annotation Specialist

LabelboxVideoBounding BoxPoint Key Point

Worked on large-scale annotation projects for self-driving vehicle datasets, including labeling pedestrians, vehicles, traffic signs, and road infrastructure in both images and videos. Tasks included bounding boxes, segmentation masks, object tracking, and quality validation to ensure high accuracy for AI training models. Collaborated with a team of annotators to maintain consistency across datasets exceeding 100,000 frames, following strict annotation guidelines and quality assurance protocols.

Worked on large-scale annotation projects for self-driving vehicle datasets, including labeling pedestrians, vehicles, traffic signs, and road infrastructure in both images and videos. Tasks included bounding boxes, segmentation masks, object tracking, and quality validation to ensure high accuracy for AI training models. Collaborated with a team of annotators to maintain consistency across datasets exceeding 100,000 frames, following strict annotation guidelines and quality assurance protocols.

2022

NER (Named Entity Recognition) Specialist for LLM Training

LabelboxImageRelationshipClassification

Annotated and labeled large-scale text datasets for training large language models, focusing on named entity recognition (NER) for entities like persons, organizations, locations, dates, and products. Tasks included entity tagging, relationship labeling, and classification to ensure high-quality training data. Reviewed and validated annotations for consistency and accuracy across 50,000+ text samples, adhering to strict annotation guidelines and quality control processes.

Annotated and labeled large-scale text datasets for training large language models, focusing on named entity recognition (NER) for entities like persons, organizations, locations, dates, and products. Tasks included entity tagging, relationship labeling, and classification to ensure high-quality training data. Reviewed and validated annotations for consistency and accuracy across 50,000+ text samples, adhering to strict annotation guidelines and quality control processes.

2020 - 2021

Education

Work History