Senior Computer Vision & Spatial Geometry Engineer

Location: New Yori, NY / Remote

Duration: Full Time

Role Overview

We are building an automated system that converts scanned architectural floor plans (PDFs)—including NYC as-built condominium filings—into structured, coordinate-based spatial data suitable for 3D modeling and GIS workflows.

This role focuses on reconstructing accurate geometry from raster scans, not extracting existing vectors and not training end-to-end black-box models. The core challenge is turning noisy, skewed, real-world blueprint scans into watertight room polygons with real-world coordinates.

We are looking for a senior engineer who is strong in classical computer vision, computational geometry, and raster-to-vector reconstruction, and who enjoys solving hard, practical problems with deterministic and explainable systems.

Key Responsibilities

Geometry & Vision Pipeline

• Design and implement a raster-to-geometry pipeline for scanned architectural PDFs
• Build robust preprocessing tools for:
• deskewing
• binarization
• noise reduction
• normalization of low-quality scans
• Isolate architectural linework (walls, boundaries) from:
• text
• dimensions
• symbols
• stamps and annotations
• Handle door gaps and broken boundaries to ensure enclosed, “watertight” regions
• Extract enclosed regions (rooms, corridors) using connected components and topology analysis
• Convert raster regions into clean polygon geometry
• contour extraction
• polygon simplification
• vertex snapping
• consistent winding and validity checks

Spatial Accuracy & Scaling

• Develop deterministic methods to convert pixel geometry into real-world X/Y coordinates
• Calibrate scale using:
• architectural dimension annotations
• scale notes when available
• Validate geometry numerically:
• closed polygons
• area consistency
• tolerance-based error detection

Text & Semantic Integration

• Integrate OCR outputs to:
• associate room labels with polygons
• parse dimension strings (feet/inches, metric)
• extract height or ceiling notes
• Map semantic text to spatial geometry using proximity and containment logic

Output & Integration

• Produce structured JSON outputs aligned with downstream 3D/GIS systems
• Ensure outputs are explainable, debuggable, and consistent across floors and documents
• Build internal visualization/debugging tools (overlays, masks, polygon previews)

What This Role Is Not

• Not prompt engineering
• Not LLM application development
• Not training large end-to-end neural networks
• Not purely academic research

This role is about deterministic geometry extraction from real-world scanned documents.

Required Technical Skills

• 5+ years of experience in computer vision, image processing, or computational geometry
• Strong command of classical CV techniques, including:
• thresholding and morphology (dilate/erode/open/close)
• edge and line detection (e.g., Hough transforms)
• connected components and region analysis
• contour tracing and polygon simplification (e.g., Douglas–Peucker)
• Solid understanding of planar geometry and numerical robustness
• Experience converting raster data into vector or polygon representations
• Strong Python skills (NumPy, OpenCV, scikit-image, etc.)
• Comfortable debugging visually and iterating on messy real-world data

Strongly Preferred

• Experience with architectural drawings, floor plans, CAD, BIM, GIS, or maps
• Familiarity with OCR systems and bounding-box–based text extraction
• Experience parsing architectural dimensions (feet/inches or metric)
• Experience validating polygon geometry (self-intersection, closure, area)
• Prior work on document image analysis or technical drawings

Nice to Have

• Experience using pretrained segmentation models to supplement classical CV
• Exposure to GIS or BIM formats (GeoJSON, IFC, IMDF)
• Knowledge of NYC as-built or Department of Buildings / Finance document conventions
• Experience building internal QA or visualization tools
• Familiarity with downstream 3D geometry pipelines