Max Koschany

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Bounding Box

Object Detection

Prompt + Response Writing (SFT)

Freelancer Overview

I am an AI data labeler with hands-on experience contributing to the Aether project through Outlier, supporting the training of computer vision models using high-quality annotated data. My work focused on image-based annotation tasks, including surface texture replacement, visual consistency checks, and accuracy validation to ensure training data met strict quality standards. Through the Aether project, I developed strong proficiency in identifying object boundaries, applying precise edits to real-world scenes, and evaluating visual outputs for realism and alignment with task instructions. I have experience working across image, text, and video data, and I am comfortable following detailed guidelines, handling edge cases, and maintaining consistency across large datasets.

Labeling Experience

Aether Project – Computer Vision Data Annotation (Scale AI)

Scale AIImageBounding BoxPoint Key Point

Contributed to the Aether project through Outlier, supporting the training of computer vision models with high-quality annotated data. Tasks included surface texture replacement on real-world scenes, precise object boundary identification, and visual accuracy validation to ensure outputs met strict quality and realism standards. Worked across image and video data to apply consistent annotations, handle edge cases, and review model-generated outputs for alignment with task guidelines. Additional responsibilities included quality checks and corrective adjustments to improve dataset reliability. Followed detailed instructions, maintained consistency at scale, and delivered annotations suitable for downstream AI training and evaluation.

Contributed to the Aether project through Outlier, supporting the training of computer vision models with high-quality annotated data. Tasks included surface texture replacement on real-world scenes, precise object boundary identification, and visual accuracy validation to ensure outputs met strict quality and realism standards. Worked across image and video data to apply consistent annotations, handle edge cases, and review model-generated outputs for alignment with task guidelines. Additional responsibilities included quality checks and corrective adjustments to improve dataset reliability. Followed detailed instructions, maintained consistency at scale, and delivered annotations suitable for downstream AI training and evaluation.

2025

Education

Work History