Construction Plans Computer Vision Intern

SARASOTA ELECTRIC LLC; Sarasota (FL); Hybrid; USD 2,066,000 - 3,444,000; Full time

Job summary

Holliday Group, LLC in Sarasota, Florida seeks a Computer Vision Intern for ForgePlan, an in-house plan-review platform. This on-site/hybrid internship is paid and focuses on building the CV layer that reads flattened architectural PDFs, detects rooms and walls, and provides code-compliant overlay guidance.

You will own a component, defend your accuracy, and present results to licensed professionals. Candidates should have Python and CV library experience, a passion for messy data, and be

Qualifications

• Currently enrolled in, or recently completed, a degree in computer science, computer engineering, electrical engineering, applied mathematics, or a related field — or equivalent demonstrable ability
• Working experience with Python and at least one computer vision or deep learning library (OpenCV, PyTorch, TensorFlow, scikit-image, or similar)
• Comfort with image processing and geometry fundamentals: convolution, thresholding, contours, transforms, coordinate systems
• Experience with version control (Git) and the habit of writing code someone else can read
• Able to work on-site in Sarasota, Florida for the majority of the term
• Genuine curiosity about a messy, unglamorous domain — the data here is scanned, rotated, inconsistently labeled, and occasionally hand-marked

Responsibilities

• Coordinate and own a computer vision component for ForgePlan’s plan-review workflow
• Defend accuracy numbers with experiments and clear documentation
• Work with engineers and domain experts to validate outputs on real job sets

Skills

Python

OpenCV

Git

Image processing

Education

Bachelor's degree in CS/CE/EE or related

Tools

PyTorch

TensorFlow

scikit-image

Job description

Holliday Group, LLC in Sarasota, Florida seeks a Computer Vision Intern for ForgePlan, an in-house plan-review platform. This on-site/hybrid internship is paid and focuses on building the CV layer that reads flattened architectural PDFs, detects rooms and walls, and provides code-compliant overlay guidance.

You will own a component, defend your accuracy, and present results to licensed professionals. Candidates should have Python and CV library experience, a passion for messy data, and be