Feladataid
- Develop applied Al models for real-time perception, classification, and situational understanding
- Work with multi-modal data
- Build scalable, low-latency inference pipelines for edge and hybrid (onboard + offline) environments
- Design and evaluate Al-based decision support systems (rule+ML or hybrid)
- Collaborate with software, platform, and integration teams to deploy on real hardware
- Maintain focus on robustness, explainability, and safety
Elvárások
- Hold an MSc or PhD in Computer Science or closely related fields in machine learning, signal processing
- Strang background in machine learning (deep learning, statistical learning, representation learning)
- Practical experience in sensor data classification, signal exploitation, or computer vision
- Familiar with Python, Pytorch,
- Strang software engineering standards (Cl/CD, testing, modularity)
- Fluent in English and German
Nice to have
- Experience in building latency-sensitive ML pipelines, model compression or optimization
- Knowledge of decision support systems or reinforcement learning
- Background in Al robustness, interpretability
Amit ajánlunk Neked
It is a matter of course for us to offer you optimal working conditions. These include, for example:
- A strong team spirit.
- An exciting, diverse, and innovative work environment and a pleasant working atmosphere with an open corporate culture and flat hierarchies.
- Hybrid working in the Munich office and remotely (e.g. from home).
As well as various benefits: https://www.thyssenkrupp-transrapid.com/de/karriere/arbeiten-bei-uns
Rólunk
thyssenkrupp Transrapid GmbH
TechCenter Control Technology
Kirsten Harling
kirsten.harling@thyssenkrupp.com
Firmen aus der Branche Personalvermittlung bitten wir höflichst, von Anfragen abzusehen.
