Develop and improve computer vision models for real-world traffic detection and tracking, owning the full cycle from data strategy and reproducible training to field validation and edge deployment.
AI Algorithm Engineer - Vision Localization Group
📜 Description
- Develop localization algorithms for autonomous driving using surround-view camera systems.
- Design deep learning models using Transformers, Graph Neural Networks, and Convolutional Neural Networks.
- Address 3D geometry, scene understanding, object matching, and spatial reasoning challenges.
- Combine learning-based methods with classical optimization and geometric algorithms.
- Own algorithm development from prototyping through deployment.
- Analyze real-world driving data and improve robustness across environmental and visibility conditions.
🛠️ Requirements
- 2+ years of practical experience developing computer vision or machine learning solutions using Python and frameworks such as PyTorch or TensorFlow.
- B.Sc. or M.Sc. in Computer Science, Electrical Engineering, or a related field, with strong academic performance.
- Solid foundation in algorithms, data structures, and computer vision and deep learning fundamentals.
- Strong analytical skills, ownership, and ability to work collaboratively.
Full job description
Our vision Localization group is responsible for accurately localizing the vehicle on a high-definition 3D map with centimeter-level precision using surround camera systems to enable autonomous driving. Our work combines advanced geometric reasoning, scene understanding, and large-scale mapping to solve one of the core challenges of autonomous vehicles in complex real-world environments.
The role involves developing algorithmic and deep learning solutions that integrate mathematical optimization, computer vision, and 3D geometry. We work with modern neural network architectures including Transformers, GNNs, and CNNs to solve problems involving scene understanding, object association, and precise spatial reasoning in dynamic environments. The HD map contains rich geometric and semantic information that is critical for safe driving and cannot always be inferred reliably from images alone under all visibility conditions or viewpoints. In addition to achieving highly accurate localization, the team is also responsible for estimating map relevance and consistency with the current world state to ensure safe and reliable autonomous driving.
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