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.