End-to-End CV Research Intern, Construction AI

Attentive.ai; San Jose (CA); On-site; USD 110,000 - 124,000; Part time

Job summary

Attentive.ai is seeking a Research Intern to advance computer vision and deep learning for automated takeoff and estimation in construction. You will own a scoped research problem end to end, framing it, running experiments, and building a prototype evaluated on real drawings.

You'll train and evaluate models for object detection, segmentation, or structured extraction, explore self-supervised learning, and present results to the team.

Qualifications

• Pursuing a Master’s or PhD in Computer Science, AI/ML, Data Science, Mathematics, Electrical Engineering, or a related field.
• Strong Python skills and at least one deep learning framework (PyTorch or TensorFlow).
• Coursework or project experience in computer vision, image processing, or machine learning.

Responsibilities

• Own one research problem end to end: framing, data, experiments, and a prototype that runs on real drawings.
• Train and evaluate deep learning models for object detection, segmentation, or structured extraction on vector and raster construction drawings.
• Explore self-supervised and representation learning on our corpus of unlabeled drawings.
• Work with multimodal signals, combining visual layout, geometry, and text.
• Design the evaluation for your problem, and present results to the research team.

Skills

Python

PyTorch

TensorFlow

Computer Vision

Machine Learning

Education

Master's or PhD candidate in CS/AI/Data Science/Math/EE

Tools

GCP

AWS

Azure

Job description

Attentive.ai is seeking a Research Intern to advance computer vision and deep learning for automated takeoff and estimation in construction. You will own a scoped research problem end to end, framing it, running experiments, and building a prototype evaluated on real drawings.

You'll train and evaluate models for object detection, segmentation, or structured extraction, explore self-supervised learning, and present results to the team.