Algorithm Engineer - REM
On-siteBeijing, Beijing, China
Job Summary
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, graph modeling, and generative methods with 3D spatial reconstruction to tackle complex urban scene challenges. Write standardized, maintainable, and testable production code in Python or C++ while participating in code reviews and driving technical iteration. Master or Ph.D. in Computer Science or related fields with 2+ years of algorithm development experience in computer vision, generative AI, and spatial modeling. Fluent in Mandarin and English with a proven track record of migrating research algorithms to mass-production pipelines.
Required Qualifications
- 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
Desired Qualifications
- 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
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