Via developer állások Németországban (programozó, fejlesztő)

• Discussing and clarifying requirements with our internal domain experts
• Design and development of the algorithms that are optimized over various layers of our software stack
• Design and development of the software that interacts with specific hardware components

Design, development, training, validation, and continuous optimisation of ML models for production-grade automotive use cases, covering the full lifecycle from initial prototype to long-term operation....

• Join a multidisciplinary team working on the forefront of generative AI applied to the engineering domain.
• Furthermore, you will develop a deep understanding of machine learning models applied to 3D geometries such as CAD files and simulations.

ARE YOU PREPARED FOR THE FUTURE? AI, AI SIR! Artificial Intelligence and machine learning are at the cutting edge when it comes to shaping next-gen mobility in areas like automated driving. But it takes real intellectual leadership and expertise to identify and then harness the AI trends and...

• Become familiar with our previously developed neural network superstructure for learning iterative algorithms
• Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference
• Investigate scaling and performance bottlenecks

• Prepare large-scale automated driving datasets for training and evaluation purposes.
• Contribute to the development and maintenance of the coding framework for data processing, model training, and validation of end-to-end driving systems.

You define the AI system strategy for ADAS: model architectures, data flows, integration concepts: Grounded in methodological expertise and validated through technical feasibility assessments....

You will design and deliver end-to-end ADAS AI models such as object detection, behavior prediction, and trajectory planning - from data analysis and model design to SOP-ready release....

• Develop and optimize our job data crawling pipeline, ensuring high-quality job data acquisition.
• Implement and refine import mapping, improving data accuracy and classification.
• Support the integration of external data sources via APIs or scraping frameworks.