, our Radar Architecture team develops advanced AI-driven radar solutions to enable intelligent perception for next-generation automotive applications. We work across the stack—from low-level radar signal understanding to multi-sensor (camera + radar) scene perception—always with a strong link to embedded deployment and real-world use cases. Our work supports production-level systems in partnership with major Tier-1s like CARIAD, Aptiv, and Continental.

Key Responsibilities

• Assist in developing and evaluating computer vision models for scene understanding using radar and camera inputs

• Develop data pre-processing algorithms for training and validation

• Support the implementation of radar-camera fusion pipelines using PyTorch

• Run model experiments on embedded platforms and analyze performance metrics

• Help visualize outputs in Bird’s Eye View (BEV), image, or 3D voxel spaces

• Collaborate with cross-functional teams and contribute to technical discussions

What We’re Looking For

• Currently pursuing a Master’s degree in Computer Science, Electrical Engineering, Robotics, or a related field

• Experience with PyTorch or TensorFlow for training deep learning models

• Familiarity with basic radar signal processing or computer vision

• Interest in embedded AI and multi-modal perception

• Strong Python skills and motivation to contribute to real-world ML systems

Bonus Points

• Familiarity with deployment tools such as TensorRT, TVM, or OpenCL

• Experience with large-scale datasets like nuScenes, Waymo Open Dataset, or K-Radar

• Exposure to 3D vision or volumetric methods for scene understanding

Why Join Us?

• Contribute to industry-relevant projects in radar perception and fusion

• Gain hands-on experience with embedded AI in a production context

• Work alongside experts in signal processing, hardware, and machine learning

• Flexible hours and hybrid work setup to support your academic schedule