About this role
Summary: The Research Intern will work on computer vision and deep learning problems for automated takeoff and estimation in construction. Responsibilities include owning a scoped research problem end-to-end, training deep learning models for object detection and segmentation, and evaluating prototypes on real data.
About Attentive.ai:
Attentive.ai builds AI for construction and field services. Our takeoff and estimating platform, Beam AI, is used by 1,200+ contractors across the US and Canada and has completed over 500,000 takeoffs. [Attentive.ai](http://attentive.ai/) is transforming the field services and construction industries with our flagship AI solution - Beam AI. Our platform empowers businesses to double their bidding capacity and accelerate growth through automation and intelligent insights. More than 1k+ businesses across the U.S. and Canada already use our products to boost sales velocity and streamline operations. We have raised $30.5M in Series B funding, accelerating our mission to make advanced AI tools accessible, practical, and impactful in the real world. We are proudly Backed by Insight Partners, Peak XV (Surge), InfoEdge, Tenacity and Vertex Ventures.
About the Role:
As a Research Intern, you'll work on the computer vision and deep learning that power automated takeoff and estimation. The core problem is unsolved: a plan set is hundreds of pages drafted to dozens of CAD conventions, where the same primitive is a wall, a hatch pattern, or a leader line depending on context a model has to infer.
You'll own one scoped research problem end to end, paired with a senior researcher — framing it, running the experiments, and building a prototype evaluated on real data. It won't be a side project; it'll be something on the team's roadmap.
What You'll Do:
- 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
What We're Looking For:
- Currently pursuing a Master's, or PhD in Computer Science, AI/ML, Data Science, Mathematics, Electrical Engineering, or a related field
- Strong foundations in Python and at least one deep learning framework (PyTorch or TensorFlow)
- Coursework or project experience in computer vision, image processing, or machine learning
- Curiosity about applied research and a willingness to iterate quickly on ideas
- Comfortable working in a fast-paced, startup-like environment with close collaboration
- Clear written and verbal communication skills
Nice to have:
- Prior research experience, publications, or projects in computer vision or deep learning
- Familiarity with vector graphics and CAD formats (SVG, DXF, DWG, IFC)
- LLM and NLP experience like structured extraction, retrieval over long documents
- Experience with cloud environments (GCP, AWS, or Azure)
How We Work:
- Empirical over theoretical — we'd rather run the experiment than argue about it
- Truth-seeking, including about our own results. A negative result reported early is worth more than a positive one defended late
- Research is judged by whether it holds up in front of 1,200 contractors, not by whether it's clever
- Honest feedback, given directly
Compensation:
The stipend range for this role is 80-90$ per hour. The final offer depends on experience and level.