Public summary
An international high-tech company based in Munich is seeking a Senior Robotic Learning Engineer to lead the architecture of data-to-model pipelines for robotic foundation models. This role involves designing systems for data collection, generation, valuation, and deployment, working closely with interdisciplinary teams to create scalable, production-grade robotic AI solutions. The position requires advanced expertise in robotics, machine learning, and engineering with strong programming skills, offering opportunities for professional growth in a diverse and innovative environment.
Location and work setup
- Location
- Munich
- Remote status
- On-site
- German requirement signal
- No German Required Detected
- Detected job language
- English
Responsibilities
Own the end-to-end system architecture for data pipelines from collection through post-training to on-robot deployment. Integrate evaluation frameworks in simulation and real hardware, ensuring reliable automatic deployment on robotic platforms. Develop data valuation metrics to guide data curation. Build synthetic and simulation-based data generation pipelines to enhance training datasets. Design scalable data collection systems for both robot-generated and human-collected data. Collaborate cross-functionally with research, engineering, and infrastructure teams to translate research needs into production-ready data and evaluation systems.
Qualifications
Master's degree or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related field, or equivalent experience. Minimum 5 years of professional experience in robotics, autonomous driving, machine learning, or data engineering with ownership of systems spanning multiple pipeline stages. Hands-on experience with imitation learning, Vision-Language-Action models, reinforcement learning, or similar robotic learning approaches. Proficient in Python programming for production systems; C++ knowledge is a plus. Strong systems-level thinking and ability to design across data pipeline boundaries. Experience with simulation platforms, data valuation techniques, human data collection tech, large-scale data pipelines, and deployment/MLOps is beneficial.