AI/ML Engineer – Architectural Drawing Understanding (SG)

Genia; Singapore; On-site; SGD 60,000 - 90,000; Full time

Benefits offered by this job

Competitive salary

Equity incentives

Career development opportunities

Job summary

A technology company in Singapore is seeking an AI/ML Engineer focused on architectural drawing understanding. The role requires expertise in computer vision and deep learning, with responsibilities including model development and integration into engineering workflows. Candidates should have a strong educational background and proven experience in building CV pipelines and ML systems. This position offers a competitive compensation package with equity incentives, encouraging talented individuals from diverse backgrounds to apply.

Qualifications

• 3+ years of hands-on experience building CV pipelines and production-ready ML models.
• Proven track record with classical CV algorithms: contour detection, edge detection.
• Strong experience training deep learning CV models: CNNs, U-Net, YOLO, etc.

Responsibilities

• Develop and optimize computer vision models for architectural drawing understanding.
• Design and train deep learning models for detection and segmentation of CAD drawing elements.
• Collaborate with cross-functional teams to integrate vision models into workflows.

Skills

Computer Vision

Deep Learning

Python

Machine Learning Frameworks

Mathematics and Geometry

Education

Bachelor's, Master's, or PhD in Computer Science or related fields

Tools

PyTorch

TensorFlow

OpenCV

Job description

AI/ML Engineer – Architectural Drawing Understanding (SG)

Responsibilities

We are seeking an AI/ML Engineer with strong expertise in Computer Vision (CV) to build intelligent systems that can interpret architectural drawings in DWG format. The role emphasizes designing and training computer vision pipelines — from classical CV methods to state-of-the-art deep learning models — to extract geometry, text, symbols, and structural information from technical drawings. While CAD format familiarity is helpful, deep CV expertise is the primary requirement.

• Develop and optimize computer vision models (classical + deep learning) for entity detection, segmentation, symbol recognition, and annotation extraction from architectural drawings.
• Apply classical CV techniques (e.g., edge detection, contour analysis, Hough transform, morphological operations) alongside deep learning models to solve vector and raster understanding tasks.
• Design and train deep learning models (e.g., CNNs, Mask R-CNN, U-Net, YOLO, DETR, Vision Transformers) for detection and segmentation of CAD drawing elements.
• Implement OCR pipelines for text and dimension extraction in drawings.
• Build robust data pipelines: preprocessing DWG files, rasterization/vectorization, augmentation, and dataset creation for supervised training.
• Benchmark, evaluate, and continuously improve model accuracy, robustness, and efficiency.
• Collaborate with cross-functional teams to integrate vision models into design automation and CAD/BIM workflows.

Qualifications

EDUCATION & BACKGROUND

• Bachelor’s, Master’s, or PhD in Computer Science, Artificial Intelligence, Computer Vision, or related fields.
• Strong foundation in mathematics, geometry, and image processing.
• 3+ years of hands-on experience building CV pipelines and production-ready ML models.
• Proven track record with classical CV algorithms (OpenCV, scikit-image): contour/edge detection, shape matching, geometric transformations, Hough transform, morphological filtering.
• Strong experience training and deploying deep learning CV models: CNNs, U-Net, Mask R-CNN, Faster R-CNN, YOLO, DETR, Vision Transformers, SAM, etc.
• Experience with OCR (e.g., Tesseract, deep-learning-based text recognition).
• Practical experience in combining classical CV with deep learning for hybrid solutions.

TECHNICAL SKILLS

• Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Strong engineering practices: Git, CI/CD, testing, Docker, and scalable inference deployment.
• Familiarity with vector graphics, CAD data formats (DWG/DXF), and computational geometry is a plus, but not mandatory.

PREFERRED SKILLS

• Knowledge of geometric deep learning or graph-based approaches for structured vector data.
• Experience with annotation tools, dataset creation, and augmentation for CV tasks.
• Familiarity with AEC (Architecture, Engineering, Construction) workflows is an advantage.

About Us

Established in 2023, Genia is dedicated to empowering the North American real estate market with generative AI. Our product, Structural CoPilot, automates the generation of structural engineering design drawings for the construction sector, enhancing efficiency and quality for engineering design firms and real estate developers.

The founding team has a deep background in the architecture and AI industries, with experience from leading internet and architectural engineering companies such as Amazon, Tencent, and ARUP. Team members hold degrees from renowned universities, including Yale, UPenn, Columbia, CMU, Duke, UCLA, and UBC. They have a proven track record of building multiple AI products from the ground up.

In early 2024, Genia successfully closed a multi-million dollar financing round with investors including a top-tier Silicon Valley venture capital firm and Europe's largest construction technology fund. We have also established strategic partnerships with several North American architectural engineering firms. The company is in a phase of rapid expansion and offers a competitive compensation package, including equity incentives for outstanding talent. We sincerely invite talented individuals from all backgrounds to join us!

Apply Now

If you are interested in joining us, send your resume and any other materials to hr [at] genia.design. We look forward to working with you soon!

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