AI Engineer – Human-to-Robot Learning (human)

The role

This person will turn multimodal, body-worn data into representations for robot learning. They will design pretraining and fine-tuning strategies for robotic foundation models and build benchmarks to test model generalization.

What you will do

• Turn multimodal, body-worn data into representations a robot can learn from.
• Solve cross-embodiment transfer, translating human motion and interaction into action spaces.
• Design pretraining and fine-tuning strategies for robotic foundation models.
• Build benchmarks that separate models that merely mimic from models that generalize.
• Turn model blind spots into requirements for data capturing devices.
• Work with hardware and systems engineering teams.

Required qualifications

• Master's or PhD in Computer Science, Machine Learning, Robotics, or comparable.
• Real, hands-on experience with multimodal foundation models (VLA, video-action, or similar).
• Experience with cross-embodiment transfer, RL based retargeting.
• Deep understanding of low-level robotics control.
• Strong grounding in imitation learning, representation learning, and self-supervised learning across sensor modalities.
• Fluency in modern machine learning frameworks and large-scale training infrastructure.
• A communicator who moves easily between researchers, engineers, and hardware teams.
• Fluent English

Nice to have

• German a plus

About the company

neura-robotics-gmbh