Expert Map Generation Engineer - Self-Driving
On-siteFrankfurt am Main, Hesse, Germany
Job Summary
Develop and optimize mapping algorithms that fuse LiDAR, radar, camera, and GNSS/IMU data to generate semantic, geometric, and topological maps for autonomous vehicles. Collaborate with perception, prediction, and planning teams to integrate mapping outputs into the full autonomy stack while designing large-scale workflows using real-world and simulated datasets. Research state-of-the-art techniques in deep SLAM and neural implicit representations, then optimize models for deployment on automotive embedded platforms with real-time constraints. Validate mapping and localization algorithms at scale with simulation teams and drive engineering excellence through modular, tested code. Contribute to the research community via publications or open-source projects in mapping and localization.
Required Qualifications
- MSc/PhD in Computer Science, Robotics, Electrical Engineering, or a related technical field
- 5+ years of industry experience
- Strong background in localization, mapping, and SLAM (visual, LiDAR, or multi-sensor)
- Experience with deep learning–based approaches for mapping and localization (e.g., neural SLAM, implicit representations, deep visual odometry)
- Proficiency in Python and C++, with hands-on experience in ML frameworks such as PyTorch or TensorFlow
- Familiarity with 3D computer vision, geometric deep learning, and multi-sensor calibration
- Familiarity with probabilistic state estimation (Kalman filters, particle filters, graph optimization)
- Track record of bringing research concepts into production-ready solutions
- Strong problem-solving skills and ability to deliver robust algorithms under real-world conditions
Desired Qualifications
- Experience in ADAS or autonomous driving localization/mapping systems
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