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

NEURA Robotics; München; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

NEURA Robotics in Munich seeks a researcher to advance multimodal foundation models for robotics. You will turn body-worn data into learnable representations and bridge human and robot embodiments.

You will collaborate with hardware and systems teams, apply imitation learning and self-supervised methods, and communicate across researchers, engineers, and hardware partners. Fluent English; German is a plus.

Qualifikationen

• Master's or PhD in Computer Science, ML, robotics or comparable field.
• Hands-on experience with multimodal foundation models.
• Experience with cross-embodiment transfer and RL-based retargeting.
• Deep understanding of low-level robotics control.
• Strong grounding in imitation, representation, and self-supervised learning across sensor modalities.
• Fluency in modern ML frameworks and large-scale training infrastructure.
• An effective communicator between researchers, engineers and hardware teams.
• Fluent English, German a plus.

Aufgaben

• Model the Human Experience by turning multimodal, body-worn data into representations a robot can learn from.
• Bridge Two Bodies by enabling cross-embodiment transfer for dissimilar robotic bodies.
• Build Foundation Models That Act via pretrained and fine-tuned strategies.
• Prove It Works by designing benchmarks that separate generalization from mimicry.
• Shape What Gets Captured Next by turning model blind spots into data requirements.
• Collaborate across hardware and systems engineering teams while focusing on data and AI.

Kenntnisse

multimodal foundation models

cross-embodiment transfer

low-level robotics control

imitation learning

representation learning

self-supervised learning

ML frameworks

communication across teams

English fluency

Ausbildung

Master's or PhD in Computer Science, ML, Robotics

Jobbeschreibung

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.