Contineu is building AI-based construction quality control solutions. We use helmet-mounted 360cameras to automatically generate 3D as-builts of the site, along with automatically marked quality issues and defects across all trades.   You'll work directly with Kanao (CTO) on the core algorithmic problems underneath this. The scope is deliberately wide and moves as the research does. Recent and near-term problems look like: Reconstruction from panoramic (e.g. INSV) video — SfM/MVS, neural reconstruction (NeRF, Gaussian splatting), depth estimation, and making any of it survive real capture conditions Registration — aligning reconstructions to each other and to the design model across time Algorithms over IFC / BIM — parsing, querying, and reasoning about building geometry and the relationships encoded in a building model Segmentation and detection on 2D and 3D construction data Whatever the highest-leverage problem is that quarter — the role follows the research, not a fixed ticket queue   What we're looking for 0–2 years, with one thing you've built that proves depth — a project, paper, competition (Kaggle, ICPC, research), or portfolio. We care more about how deep you went on one thing than how many things you've touched. Strong programming fundamentals — data structures, algorithms, complexity. You can reason about why your code is correct and what it costs. Strong ML Fundamentals. You can explain how the methods you've used actually work. Bias to action. Fast iteration, low ego, comfortable being wrong quickly and moving on. Bonus: hands-on with 3D, computer vision, or geometry — point clouds, multi-view geometry, camera models, SLAM, registration, anything in this space.