Productionize 3D scene-understanding systems for embodied AI, translating perception research into reliable pipelines that label environments and support downstream simulation, policy learning, and customer workflows.
Outstanding Graduate Algorithm Developer - Vision Localization Group
📜 Description
- Develop localization algorithms for autonomous driving using surround-camera systems.
- Design and implement 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
- B.Sc. in Computer Science, Software Engineering, or a related field from a leading university, with a proven record of excellence.
- M.Sc. with a research thesis is an advantage.
- Strong programming skills in Python and C++.
- Solid foundation in algorithms, data structures, mathematics, and computer vision or deep learning fundamentals.
- Strong analytical skills, ownership, and ability to collaborate effectively.
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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