Algorithm Engineer - REM

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

• 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.
• Model road geometry, semantic features, lane connections and global road topology through spatial reasoning, topological learning networks and graph networks.
• Combine deep learning and graph modeling with generative methods (e.g. diffusion, structured prediction) and 3D spatial reconstruction to tackle complex urban scene challenges.
• Write standardized, maintainable and testable production code with Python/C++, participate in code review and drive team technical iteration.

What we expect from you

• Master or Ph.D. in Computer Science, Electronic Engineering, Robotics or related majors.
• 2+ years algorithm development experience in computer vision, topological / graph learning, generative AI, spatial modeling, trajectory mining.
• Comfortable with basic geometry and spatial data representation (coordinates, curves, connectivity).
• Experience with at least one of: topological learning networks, generative models (diffusion / flow matching), or 3D point-cloud / scene reconstruction.
• Solid programming and algorithm capabilities with Python or C/C++; proficient in at least one deep learning framework (PyTorch / TensorFlow preferred).
• Fluent oral and written communication in both Mandarin and English, excellent team player.

Nice-to-have

• In-depth understanding of CNN, GNN, Transformer, object detection, semantic segmentation and generative AI.
• Familiar with topological learning networks like MapTR, and related lane / road topology modeling methods.
• Experience with diffusion models, generative AI, or structured output generation for maps, layouts, graphs, or splines.
• Experience with 3D point-cloud reconstruction, registration, or multi-view spatial fusion.
• Experience with mass vehicle trajectory aggregation and crowdsourced perception data processing.
• Basic exposure to GIS, HD maps, SLAM or ADAS lightweight vector map development.
• Proven track record of migrating academic research algorithms to mass-production pipelines.