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# Senior AI Research Engineer Learning-Based Robotics

- **Company:** Agile Robots Ag
- **Location:** München, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Python (Programming Language), Machine Learning, Robotic Automation Software, Reinforcement Learning, Pytorch, Deep Learning, Information Technology, Enterprise Integration, GPT
- **Published:** July 22, 2026
- **Apply:** https://www.adzuna.de/details/5727312987

## About the Role

The AI Research Division of Agile Robots is looking for a Senior AI Engineer with strong experience in learning-based methods for robotics and sequential decision-making, focused on developing predictive and learning-based components for embodied systems in collaboration with robotics, perception, and systems teams., * Professional Experience: Minimum of 4 years of professional experience in AI, deep learning, and/or robotics.

 * Sequential Learning: Strong background in sequential prediction, dynamics modelling, or model-based learning.
 * Temporal Data: Demonstrated experience building and training deep learning models for temporal data such as video, trajectories, or state sequences.
 * ML Engineering: Solid Python programming skills and hands-on experience with PyTorch.
 * Embodied Systems: Hands-on experience with robotic systems or embodied agents.
 * Education: MSc or PhD in Robotics, AI, Computer Science, or a related field.

Beneficial Skills

 * Research Output: Publications or preprints in top-tier ML or robotics venues such as NeurIPS, ICLR, ICML, CoRL, RSS, or ICRA.
 * Advanced Modelling: Experience with latent dynamics models, model-based reinforcement learning, or predictive control.
 * Sequence Architectures: Familiarity with transformer-based or diffusion-based sequence models.
 * Applied Robotics ML: Prior experience applying machine-learning methods in robotics contexts.

## Description

* Learning Models: Research and develop learning-based models for robotic and embodied systems.
 * Predictive Modelling: Design, train, and evaluate predictive or dynamics-related models.
 * Pipeline Integration: Integrate learned models with control, planning, or policy-learning pipelines.
 * Experimental Evaluation: Design robotic experiments and conduct scientific evaluation of model performance.
 * Collaboration: Collaborate closely with Robotics, Perception, and Systems teams.
 * Research Transfer: Contribute to publications, prototypes, and technology transfer into products.


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