Role description
Attentive.aiAbout 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 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 Scientist, you'll advance computer vision, deep learning, and NLP 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'd be an early member of the US research team, which means real influence over the directions we pursue. You'll own research problems end to end and take models to production.
What You'll Do
- Own research problems end to end: framing, data, experiments, ablations, and the model that ships
- Advance deep learning models for object detection, semantic segmentation, and structured extraction on vector and raster construction drawings
- Build multimodal systems combining visual layout, geometry, and text
- Design evaluation frameworks to continuously improve the model
- Stay current with computer vision and ML research and evaluate what's relevant to construction-industry problems
What We're Looking For
- 2–8 years of applied machine learning or AI research experience with a strong focus on computer vision and deep learning. We'll calibrate level and compensation to what you've built
- Depth in at least one of: object detection, image segmentation, image processing, OCR and layout analysis, self-supervised or representation learning, or multimodal / vision-language models
- Experience training and evaluating neural networks at scale, and at least one model you've taken to production
- Strong Python and PyTorch, across the modeling, training loop and the data pipeline
- Practical GPU and distributed training experience
- Judgment about data: what to label, what to synthesize, what to discard
- The ability to read current research critically and say what's worth trying
Nice To Have
- Master's or PhD in Computer Science, Machine Learning, Electrical Engineering, Mathematics, or a related field
- Familiarity with vector graphics and CAD formats (SVG, DXF, DWG, IFC)
- LLM and NLP experience like structured extraction, retrieval over long documents
- Model optimization: quantization, pruning, knowledge distillation
- Publications or open-source contributions in computer vision or deep learning
- 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
Sponsorship
We sponsor visas.
If you need sponsorship now or in the future, please apply.