Beijing
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. We adopt learning-based algorithms to reconstruct structured road layers from mass vehicle driving data. Our team leverages computer vision, graph modeling and computational geometry to build fully automated map production pipelines, with rapid iteration as our core value.
What will your job look like:
- Develop learning-based algorithms to reconstruct structured road vector data using mass crowdsourced vehicle perception records, REM and multi-modal sensor inputs.
- Model road geometry, semantic features, lane connections and global road topology through spatial reasoning and graph networks.
- Combine deep learning, graph modeling and computational geometry to tackle complex urban scene challenges.
- Build scalable automated pipelines for crowdsourced data aggregation, model validation, map optimization and incremental map updates.
- Continuously optimize model accuracy, robustness and generalization under occlusion, variable illumination and unmarked roads.
- 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.
- 3+ years’ algorithm development experience in computer vision, spatial modeling, trajectory mining or robotics.
- Solid programming and algorithm capabilities with Python or C/C++; proficient in at least one deep learning framework (PyTorch / TensorFlow preferred).
- Hands-on experience delivering production-level deep learning or visual perception systems.
- Able to independently research ambiguous technical bottlenecks and deliver practical engineering solutions.
- Fluent oral and written communication in both Mandarin and English, excellent team player.
Nice to have:
- Familiar with topological learning networks: MapTR, VAD, LaneGAP, TopoNet, as well as image stitching and vectorization algorithms.
- Experience with mass vehicle trajectory aggregation and crowdsourced perception data processing.
- Basic exposure to GIS, computational geometry, SLAM or ADAS lightweight vector map development.
- In-depth understanding of CNN, GNN, Transformer, object detection, semantic segmentation and generative AI.
- Proven track record of migrating academic research algorithms to mass-production pipelines.
