Senior AI Research Engineer Learning-Based Robotics

Senior AI Research Engineer Learning-Based Robotics

Agile Robots

Munich, Germany

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.

Your Responsibilities

• 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.

Essential Skills

• 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.