Algorithm Engineer - REM

Team: Algorithms

Location: Beijing, China

Commitment: Full time

Workplace Type: onsite

What will you job look like:

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. 

All you need is:

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.