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

Your Mission & Challenges 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. What We Can Look Forward To 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.