Beijing
Full time
About us:
Mobileye is a global leader in Physical AI, developing computer vision, software, and hardware to power autonomous driving solutions and humanoid robots. Our mission is to bring autonomy into the real world by building intelligent technologies that are designed to expand accessibility and enhance everyday life, from advanced driver assistance systems (ADAS) and autonomous driving to robotics and humanoids.
Built on decades of automotive innovation and industrial-scale AI expertise, Mobileye technologies power more than 230 million vehicles worldwide. With teams around the world, we continue to innovate at the forefront of autonomous systems, advancing the next generation of Physical AI for the real world.
About the team:
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 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.
