Computer Vision Engineer

Ryzlabs; Buenos Aires; A distancia; ARS 137.587.000 - 229.312.000; Jornada completa

Descripción de la vacante

Ryz Labs is partnering with a client building an AI-powered platform for automated quantity takeoff in heavy civil and highway construction. We are seeking a Senior Computer Vision / ML Engineer to own the “what is it?” problem—identifying materials, surfaces, and construction features from drawings, PDFs, aerial imagery, and satellite imagery.

You will work with domain experts to create ground truth, build reliable, priceable quantities, and develop production CV/ML pipelines, data labeling,

Formación

• 5+ years building and shipping production Computer Vision / ML systems.
• Strong Python and PyTorch experience.
• Production experience with detection and segmentation on real-world imagery.
• Experience with aerial, satellite, drone, or comparable raster imagery.
• Strong knowledge of OpenCV and classical CV techniques.
• Experience with messy, domain-specific data and human-in-the-loop systems.
• Understanding of PDF internals, vector geometry, and/or document AI, or the ability to learn quickly.
• Strong written and verbal English.

Responsabilidades

• Build CV/ML pipelines for engineering drawings, PDFs, aerial and satellite imagery.
• Extract information from both vector and raster PDFs, including degraded/scanned drawings.
• Develop detection and segmentation models for materials and infrastructure such as asphalt, concrete, pavement, drainage, pipes, signs, guardrails, and lane markings.
• Combine information from drawings, specifications, and imagery, identifying discrepancies between sources.
• Build the ML infrastructure around the models: labeling, datasets, evaluation, regression testing, confidence, and provenance.
• Work directly with domain experts to create ground truth and improve the system.
• Prioritize reliability and completeness—“not determinable” is better than a low-confidence guess.

Conocimientos

Python

PyTorch

OpenCV

Detection

Aerial imagery

PDF internals

English (written)

Descripción del empleo

Contract directly with the client
Remote - Full-time

Only for candidates based in Latam

We are looking for a Computer Vision Engineer. Ryz Labs is partnering with a client building an AI-powered platform for automated quantity takeoff in heavy civil and highway construction.

As a Senior Computer Vision / ML Engineer, you'll own the “what is it?” problem—identifying materials, surfaces, and construction features from engineering drawings, PDFs, aerial imagery, and satellite imagery.

You'll work closely with another engineering role focused on geometry and measurement, turning your detections into reliable, priceable quantities.

What You'll Do

• Build CV/ML pipelines for engineering drawings, PDFs, aerial and satellite imagery.
• Extract information from both vector and raster PDFs, including degraded/scanned drawings.
• Develop detection and segmentation models for materials and infrastructure such as asphalt, concrete, pavement, drainage, pipes, signs, guardrails, and lane markings.
• Combine information from drawings, specifications, and imagery, identifying discrepancies between sources.
• Build the ML infrastructure around the models: labeling, datasets, evaluation, regression testing, confidence, and provenance.
• Work directly with domain experts to create ground truth and improve the system.
• Prioritize reliability and completeness—“not determinable” is better than a low-confidence guess.

What We're Looking For

Required:

• 5+ years building and shipping production Computer Vision / ML systems.
• Strong Python and PyTorch experience.
• Production experience with detection and segmentation on real-world imagery.
• Experience with aerial, satellite, drone, or comparable raster imagery.
• Strong knowledge of OpenCV and classical CV techniques.
• Experience with messy, domain-specific data and human-in-the-loop systems.
• Understanding of PDF internals, vector geometry, and/or document AI, or the ability to learn quickly.
• Strong written and verbal English.

Nice to have:

• Experience with geospatial imagery, construction, document AI/OCR, CAD vectorization, or autonomous vehicle perception.
• 3D experience with photogrammetry, point clouds, terrain/surface models, or cut & fill.
• Experience across both document vision and aerial/geospatial CV.