We're building a lightweight 2D vector map system for intelligent driving and the next-generation map reconstruction stack. We adopt learning-based algorithms to reconstruct structured road layers from mass vehicle driving data. Our team combines computer vision, topological / graph learning, and generative spatial modeling to build fully automated map production pipelines, with rapid iteration as our core value.
What you’ll do
1. Develop learning-based algorithms to reconstruct structured road vector data and next-generation map outputs using mass crowdsourced vehicle perception records and multi-modal sensor inputs.
2. Model road geometry, semantic features, lane connections and global road topology through spatial reasoning, topological learning networks and graph networks.
3. Combine deep learning and graph modeling with generative methods (e.g. diffusion, structured prediction) and 3D spatial reconstruction to tackle complex urban scene challenges.
4. Write standardized, maintainable and testable production code with Python/C++, participate in code review and drive team technical iteration.
What we expect from you
1. Master or Ph.D. in Computer Science, Electronic Engineering, Robotics or related majors.
2. 2+ years algorithm development experience in computer vision, topological / graph learning, generative AI, spatial modeling, trajectory mining.
3. Comfortable with basic geometry and spatial data representation (coordinates, curves, connectivity).
4. Experience with at least one of: topological learning networks, generative models (diffusion / flow matching), or 3D point-cloud / scene reconstruction.
5. Solid programming and algorithm capabilities with Python or C/C++; proficient in at least one deep learning framework (PyTorch / TensorFlow preferred).
6. Fluent oral and written communication in both Mandarin and English, excellent team player.
Nice-to-have
1. In-depth understanding of CNN, GNN, Transformer, object detection, semantic segmentation and generative AI.
2. Familiar with topological learning networks like MapTR, and related lane / road topology modeling methods.
3. Experience with diffusion models, generative AI, or structured output generation for maps, layouts, graphs, or splines.
4. Experience with 3D point-cloud reconstruction, registration, or multi-view spatial fusion.
5. Experience with mass vehicle trajectory aggregation and crowdsourced perception data processing.
6. Basic exposure to GIS, HD maps, SLAM or ADAS lightweight vector map development.
7. Proven track record of migrating academic research algorithms to mass-production pipelines.
