AI Engineer – Human-to-Robot Learning

Model the Human Experience: Turn multimodal, body-worn data into representations a robot can learn from.

Bridge Two Bodies: Solve cross-embodiment transfer, translating human motion and interaction into action spaces a robot with a completely different body can use.

Build Foundation Models That Act: Design pretraining and fine-tuning strategies for robotic foundation models.

Prove It Works: Build the benchmarks that separate models that merely mimic from models that generalize.

Shape What Gets Captured Next: Turn model blind spots into sharp, concrete requirements for data capturing devices.

Cross-Functional Collaboration: Work shoulder to shoulder with hardware and systems engineering teams while staying laser-focused on the data and AI side.

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, German a plus.