Machine learning expert állások Németországban (programozó, fejlesztő)

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

• Develop and optimize data pipelines and algorithms within the Snowflake environment.
• Manage code repositories, automate deployment processes, and collaborate with team members using Azure DevOps for continuous integration and machine learning model improvement.

NEW SOLUTIONS EMERGE WHEN THINKING ISN'T HARD-WIRED. More than 90% of automotive innovations are based on electronics and software. That's why creative freedom and lateral thinking are so important in the pursuit of truly new solutions. Naturally, this includes flexible working models, so that our...

Architect the ML Platform: Design, implement, and maintain a robust MLOps infrastructure that enables researchers to move seamlessly from local experimentation to global production....

As a Research Engineer, you will develop cutting-edge Vision-Language-Action (VLA) architectures that empower AI agents to interpret human instructions and act autonomously in complex environments....

INNOVATION IS IMAGINING WHAT NO ONE ELSE CAN. At the BMW Group, everything starts with passion. It turns a profession into a vocation. It drives us to keep reinventing mobility and bring innovative ideas onto the roads. Enthusiasm for joint projects turns a team into a strong unit where every...

INNOVATION IS IMAGINING WHAT NO ONE ELSE CAN. 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...

• Contribute to pioneering technical advances to the field of foundation models on structured data, as well as exploringthe benef world models for solving business problems.
• Execute scientific experiments on internal as well as public data.

• Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM, TEM, EBSD).
• Learning representations that are robust to scale changes, modality shifts, and domain differences across instruments and laboratories.

• Developing parallel and distributed algorithms for processing ultra-large 3D images across multiple GPUs
• Designing and adapting scalable deep learning models for segmentation and landmark detection (e.g. U-Net variants, Transformers)