Mikhail Eyck Javines

Key Skills

Software

Labelbox

Don't disclose

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Audio

Top Label Types

Bounding Box

Text Summarization

Mapping

Tracking

Classification

Freelancer Overview

With over five years of experience in logistics, operations, and back-office support, I have developed strong skills in data analysis, quality control, and process optimization—key foundations for data labeling and AI training data roles. My background includes managing inventory systems, tracking performance metrics, maintaining accurate records, and ensuring data integrity across various platforms such as Microsoft Office, Google Sheets, Hubspot, and Salesforce. I am detail-oriented and adept at interpreting and organizing large datasets, conducting audits, and generating reports to support business decisions. I excel at collaborating with cross-functional teams to ensure high-quality outcomes and am eager to apply my analytical abilities and technical expertise to contribute to precise and efficient AI data annotation projects.

Labeling Experience

Video Annotator and Events Labeler

Don T DiscloseVideoTracking

Engaged in object ID tracking, action recognition, and event priming for video data. Ensured accuracy by assigning unique IDs to objects and filling interpolation gaps for smoother transitions. Marked exact moments for events' beginnings, helping to train AI to understand sequence and timing. Contributed to advancing AI with precise annotations for action recognition within video frames. • Assigned IDs to the same object appearing at different frames. • Conducted interpolation for gap-filling in object tracking. • Labeled actions within specific time segments of the video. • Primed events by marking their exact starting times.

Engaged in object ID tracking, action recognition, and event priming for video data. Ensured accuracy by assigning unique IDs to objects and filling interpolation gaps for smoother transitions. Marked exact moments for events' beginnings, helping to train AI to understand sequence and timing. Contributed to advancing AI with precise annotations for action recognition within video frames. • Assigned IDs to the same object appearing at different frames. • Conducted interpolation for gap-filling in object tracking. • Labeled actions within specific time segments of the video. • Primed events by marking their exact starting times.

2025

Audio Transcriptionist/Speech Attribute Labeler

Don T DiscloseAudioClassification

Performed comprehensive audio labeling and transcription involving speaker diarization and demographic tagging. Applied linguistic knowledge consistent with regional grammar in transcriptions for diverse voices. Enhanced ASR capabilities by marking linguistic variables such as pitch, tempo, and energy. Optional tasks included emotional contextualization and non-verbal sound tagging. • Conducted speaker diarization with unique speaker IDs. • Labeled gender and age categories for demographic balance. • Identified regional accents for improved ASR models. • Annotated non-speech sounds to aid NLP models.

Performed comprehensive audio labeling and transcription involving speaker diarization and demographic tagging. Applied linguistic knowledge consistent with regional grammar in transcriptions for diverse voices. Enhanced ASR capabilities by marking linguistic variables such as pitch, tempo, and energy. Optional tasks included emotional contextualization and non-verbal sound tagging. • Conducted speaker diarization with unique speaker IDs. • Labeled gender and age categories for demographic balance. • Identified regional accents for improved ASR models. • Annotated non-speech sounds to aid NLP models.

2025 - 2025

Data annotator for a Real Estate firm

LabelboxImageBounding BoxText Summarization

This 6-month Real Estate Data Annotation project is designed to create a high-quality dataset for training advanced machine learning models by labeling over 10,000 property images and their corresponding text descriptions. The core scope involves performing detailed labeling tasks such as drawing bounding boxes on key features (fireplaces, kitchen islands), classifying images by room type and condition (Kitchen, Newly Renovated), and performing named entity recognition on text to identify amenities, materials, and brand names. The project duration is structured into a 1-week pilot phase for training, a 6-month full-blown annotation period, and a 1-week final review. To ensure the highest level of accuracy, strict quality measures are required, including a detailed guidelines document, consensus reviews for uncertain cases, and regular weekly audits to maintain an individual accuracy rate of 98% or higher.

This 6-month Real Estate Data Annotation project is designed to create a high-quality dataset for training advanced machine learning models by labeling over 10,000 property images and their corresponding text descriptions. The core scope involves performing detailed labeling tasks such as drawing bounding boxes on key features (fireplaces, kitchen islands), classifying images by room type and condition (Kitchen, Newly Renovated), and performing named entity recognition on text to identify amenities, materials, and brand names. The project duration is structured into a 1-week pilot phase for training, a 6-month full-blown annotation period, and a 1-week final review. To ensure the highest level of accuracy, strict quality measures are required, including a detailed guidelines document, consensus reviews for uncertain cases, and regular weekly audits to maintain an individual accuracy rate of 98% or higher.

2025 - 2025

Education

Work History