About
Rudus is an AI-powered takeoff platform for structural and site concrete. We accelerate concrete takeoffs by 70%+ with AI trained specifically on structural and civil drawings- counting, measuring, and reading sheets so estimators can focus on winning bids.
The Role
You'll own the research behind our drawing-understanding stack: detecting structural and site elements on noisy real-world plan sets, pattern-matching footings and curb runs, interpreting schedules and cross-sheet details, and comparing drawing revisions. Your models ship into production and get evaluated by professional estimators on real bids — feedback loops measured in days, not review cycles.
What You’ll Do
Advance detection and autocomplete models for structural elements (footings, slabs, columns, walls) and site elements (curbs, paving, sidewalks, joint layouts)
Build document-understanding systems for schedules, specs, and section details, including cross-sheet reasoning
Solve geometry problems: auto-scale detection, boundary completion, smart exclusions, revision diffing
Design training and eval pipelines on customer drawing data, improving accuracy project over project
What We’re Looking For
PhD (or equivalent research experience) in computer vision, ML, or a related field
Track record turning research into working systems- strong Python required
Experience with document AI, object detection, vectorized/CAD-like data, or multimodal LLMs
Excited by messy real-world data over benchmark leaderboards