Key Skills

Software

Labelbox

Appen

SuperAnnotate

Top Subject Matter

Computer Vision/Multisector AI Models

Evaluation and Quality Assessment in Data Labeling

Generative AI

Top Data Types

Image

Top Task Types

Segmentation

Classification

Freelancer Overview

Lead AI Trainer and Data Labeling Strategist | Alignerr. Core strengths include Labelbox, Appen, and SuperAnnotate. Education includes Doctor of Philosophy, University of Wisconsin, Madison (2019) and Master of Science, University of Wisconsin, Madison (2014). AI-training focus includes data types such as Image and labeling workflows including Segmentation and Classification.

Labeling Experience

Lead AI Trainer and Data Labeling Strategist | Alignerr

LabelboxImageSegmentation

Directed end-to-end data labeling and annotation pipelines for large-scale computer vision datasets in various sectors, focusing on image data. Developed scalable workflows and introduced automated error-detection, leading research-driven optimization projects to ensure high annotation precision. Trained and managed global labeling teams, and collaborated with engineers for standard improvement on annotated data quality. • Implemented custom data validation metrics to maintain consistency across millions of samples. • Integrated human-in-the-loop frameworks within automated workflows. • Led systematic error detection models increasing data accuracy. • Established quality assurance training for 150+ annotators.

Directed end-to-end data labeling and annotation pipelines for large-scale computer vision datasets in various sectors, focusing on image data. Developed scalable workflows and introduced automated error-detection, leading research-driven optimization projects to ensure high annotation precision. Trained and managed global labeling teams, and collaborated with engineers for standard improvement on annotated data quality. • Implemented custom data validation metrics to maintain consistency across millions of samples. • Integrated human-in-the-loop frameworks within automated workflows. • Led systematic error detection models increasing data accuracy. • Established quality assurance training for 150+ annotators.

2015 - Present

Project Contributor | Project Coffee

SuperannotateImageClassification

Participated in a high-intensity dataset labeling initiative involving tagging, video analysis, and image editing for multimodal dataset creation. Provided human feedback and completed calibration to support generative AI training. Delivered precise labeling for short-form video and image data in research environments. • Contributed human feedback used for AI model development. • Performed short-form video and image editing. • Achieved qualification and calibration requirements. • Aided in advanced multimodal dataset preparation.

Participated in a high-intensity dataset labeling initiative involving tagging, video analysis, and image editing for multimodal dataset creation. Provided human feedback and completed calibration to support generative AI training. Delivered precise labeling for short-form video and image data in research environments. • Contributed human feedback used for AI model development. • Performed short-form video and image editing. • Achieved qualification and calibration requirements. • Aided in advanced multimodal dataset preparation.

2025 - 2025

Senior Quality Evaluator and Testing Analyst | Test Gorillas

AppenImageClassification

Conducted quality evaluation and structured testing of large contributor groups in controlled labeling environments for visual annotation tasks. Designed calibration and assessment methods to measure annotator consistency and developed contributor training processes. Supported benchmark creation for large-scale AI datasets applied in production. • Orchestrated structured feedback and evaluation systems. • Advanced onboarding and qualification stages for annotators. • Assessed annotator performance and visual comprehension. • Contributed to the improvement of real-world dataset benchmarks.

Conducted quality evaluation and structured testing of large contributor groups in controlled labeling environments for visual annotation tasks. Designed calibration and assessment methods to measure annotator consistency and developed contributor training processes. Supported benchmark creation for large-scale AI datasets applied in production. • Orchestrated structured feedback and evaluation systems. • Advanced onboarding and qualification stages for annotators. • Assessed annotator performance and visual comprehension. • Contributed to the improvement of real-world dataset benchmarks.

2012 - 2015

Education

Work History