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D

Derek Grover

Digital Imaging Technician (DIT) / Computer Vision & Image Analysis Specialist / Cinematic Imaging Systems / Camera Systems Specialist / Cinematic Imaging Specialist / Digital Camera Operation / HD Terminal Equipment Specialist / High Definition Production Specialist / Photographic Content Specialist

USA flagCharleston, Usa
$20.00/hrEntry Level

Key Skills

Software

No software listed

Top Subject Matter

Computer Vision – Image Annotation, Evaluation & Model Training
Media & Entertainment – Cinematography, Digital Imaging & Post-Production Workflows
AI Training Data – Image Generation, Prompt Engineering & Visual Quality Control

Top Data Types

ImageImage

Top Task Types

No task types listed

Freelancer Overview

Camera Systems Specialist. Brings 32+ years of professional experience across complex professional workflows, research, and quality-focused execution. My experience in AI training data and labeling is grounded in over two decades of high-end cinematography and Digital Imaging Technician (DIT) work in Hollywood, where I operated as an image analyst responsible for evaluating, correcting, and validating visual data under real-world production constraints. I have extensive experience assessing image fidelity, exposure accuracy, color science, spatial relationships, and continuity across complex multi-camera systems. This directly translates to high-precision data annotation tasks such as image quality evaluation, bounding box accuracy, segmentation validation, visual consistency checks, and reconstruction fidelity analysis. My background includes designing and managing digital imaging workflows at scale, ensuring data integrity, and maintaining strict quality control standards—skills that align directly with AI training requirements. In addition, I bring advanced capabilities in synthetic image creation, lighting design, and artistic stylization, allowing me to both generate and critically evaluate training data. I understand how visual inputs are constructed—from light transport and material response to perceptual depth and atmospheric layering—which enables me to identify subtle errors, edge cases, and model weaknesses that typical annotators may miss. My work with AI-generated imagery, including prompt engineering and controlled visual outputs, further strengthens my ability to produce consistent, high-quality labeled datasets. This combination of technical imaging expertise and applied AI workflow experience positions me to contribute at a high level to training, validation, and refinement of computer vision models.

Entry LevelEnglish

Labeling Experience

Project Hedgehog / Handshake AI

ImagePrompt Response Writing SFT
Ongoing AI training and data labeling work under Project Hedgehog (Handshake AI), focused on image-based prompt/response evaluation and fine-tuning support. Responsibilities include assessing model outputs for visual accuracy, coherence, and adherence to prompt intent across a wide range of scenarios including photoreal reconstruction, object detection, and scene interpretation. Tasks involve identifying defects, inconsistencies, and edge cases in generated images, as well as validating spatial relationships, scale accuracy, and contextual correctness. Applied advanced visual analysis techniques derived from professional cinematography and Digital Imaging Technician (DIT) experience, including evaluation of lighting, exposure, color accuracy, and perceptual depth. Contributed to improving model performance through structured feedback, comparative analysis (A/B testing), and consistent adherence to strict quality guidelines and task specifications.

Ongoing AI training and data labeling work under Project Hedgehog (Handshake AI), focused on image-based prompt/response evaluation and fine-tuning support. Responsibilities include assessing model outputs for visual accuracy, coherence, and adherence to prompt intent across a wide range of scenarios including photoreal reconstruction, object detection, and scene interpretation. Tasks involve identifying defects, inconsistencies, and edge cases in generated images, as well as validating spatial relationships, scale accuracy, and contextual correctness. Applied advanced visual analysis techniques derived from professional cinematography and Digital Imaging Technician (DIT) experience, including evaluation of lighting, exposure, color accuracy, and perceptual depth. Contributed to improving model performance through structured feedback, comparative analysis (A/B testing), and consistent adherence to strict quality guidelines and task specifications.

2026 - Present

Project Hedgehog / Handshake AI

ImageFine Tuning
End-to-end image training and fine-tuning project focused on high-fidelity visual data evaluation and synthetic image generation. Work includes detailed image annotation, bounding box placement, segmentation validation, and reconstruction fidelity analysis across varied datasets. Leveraged professional cinematography and Digital Imaging Technician (DIT) experience to evaluate exposure, color accuracy, lighting consistency, spatial relationships, and perceptual depth. Regularly performed A/B image comparisons, defect detection, and edge-case identification to improve model performance. Additionally contributed to dataset creation through controlled image generation using advanced prompt engineering, lighting design, and artistic stylization techniques. Applied real-world principles of light transport, material response, and atmospheric layering to produce consistent, high-quality training data. Maintained strict quality control standards, including multi-pass validation, consistency checks, and adherence to annotation guidelines to ensure dataset integrity suitable for fine-tuning computer vision models.

End-to-end image training and fine-tuning project focused on high-fidelity visual data evaluation and synthetic image generation. Work includes detailed image annotation, bounding box placement, segmentation validation, and reconstruction fidelity analysis across varied datasets. Leveraged professional cinematography and Digital Imaging Technician (DIT) experience to evaluate exposure, color accuracy, lighting consistency, spatial relationships, and perceptual depth. Regularly performed A/B image comparisons, defect detection, and edge-case identification to improve model performance. Additionally contributed to dataset creation through controlled image generation using advanced prompt engineering, lighting design, and artistic stylization techniques. Applied real-world principles of light transport, material response, and atmospheric layering to produce consistent, high-quality training data. Maintained strict quality control standards, including multi-pass validation, consistency checks, and adherence to annotation guidelines to ensure dataset integrity suitable for fine-tuning computer vision models.

2026 - Present

Education

H

Hollywood Productions

IATSE 600, Film & Television

IATSE 600
1989 - 2002

Work History

T

The Sinner

Digital Imaging Technician

Los Angeles
2017 - 2021
W

Warner Brothers Films

Digital Imaging Technician

Los Angeles
2005 - 2005