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
1. 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.
2. Model road geometry, semantic features, lane connections and global road topology through spatial reasoning, topological learning networks and graph networks.
3. Combine deep learning and graph modeling with generative methods (e.g. diffusion, structured prediction) and 3D spatial reconstruction to tackle complex urban scene challenges.
4. Write standardized, maintainable and testable production code with Python/C++, participate in code review and drive team technical iteration.
What we expect from you
1. Master or Ph.D. in Computer Science, Electronic Engineering, Robotics or related majors.
2. 2+ years algorithm development experience in computer vision, topological / graph learning, generative AI, spatial modeling, trajectory mining.
3. Comfortable with basic geometry and spatial data representation (coordinates, curves, connectivity).
4. Experience with at least one of: topological learning networks, generative models (diffusion / flow matching), or 3D point-cloud / scene reconstruction.
5. Solid programming and algorithm capabilities with Python or C/C++; proficient in at least one deep learning framework (PyTorch / TensorFlow preferred).
6. Fluent oral and written communication in both Mandarin and English, excellent team player.
Nice-to-have
1. In-depth understanding of CNN, GNN, Transformer, object detection, semantic segmentation and generative AI.
2. Familiar with topological learning networks like MapTR, and related lane / road topology modeling methods.
3. Experience with diffusion models, generative AI, or structured output generation for maps, layouts, graphs, or splines.
4. Experience with 3D point-cloud reconstruction, registration, or multi-view spatial fusion.
5. Experience with mass vehicle trajectory aggregation and crowdsourced perception data processing.
6. Basic exposure to GIS, HD maps, SLAM or ADAS lightweight vector map development.
7. Proven track record of migrating academic research algorithms to mass-production pipelines.
Skills Required
- 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, or spatial modeling
- Comfortable with geometry and spatial data representation (coordinates, curves, connectivity)
- Experience with at least one: 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 or TensorFlow)
- Fluent oral and written communication in Mandarin and English
- In-depth understanding of CNN, GNN, Transformer, object detection, semantic segmentation and generative AI
- Familiarity with topological learning networks like MapTR and lane/road topology modeling methods
- Experience with diffusion models, structured output generation, or generative AI for maps/layouts
- 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
What We Do
Mobileye is leading the mobility revolution with its autonomous-driving and driver-assistance technologies, harnessing world-renowned expertise in computer vision, machine learning, mapping, and data analysis. Founded in 1999, Mobileye has pioneered such groundbreaking technologies as REM™ crowdsourced mapping, True Redundancy™ sensing, and the RSS™ safety model. These technologies are driving the ADAS and AV fields towards the future of mobility – enabling self-driving vehicles and mobility solutions, powering industry-leading advanced driver-assistance systems and delivering valuable intelligence to optimize mobility infrastructure. Mobileye technology is used in over 170 million vehicles worldwide. In 2022, Mobileye became an independent company while still being majority-owned by Intel. Mobileye’s headquarters and R&D center are based in Jerusalem, with additional offices across Israel and around the world.
Why Work With Us
Our technology enables self-driving vehicles and mobility solutions, powers industry-leading advanced driver assistance systems, and delivers valuable intelligence to optimize mobility infrastructure.
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