Neura Robotics

Cognitive robots with perception, manipulation, collaboration

AI Engineer - Human-to-Robot Learning

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeNo salary listedIn-person

About the Job

Requirements

A Master's or PhD degree in Computer Science, Machine Learning, Robotics, or a comparable field.
Practical experience with multimodal foundation models, such as vision-language-action or video-action models.
Experience with cross-embodiment transfer and reinforcement-learning-based retargeting.
Deep understanding of low-level robotics control.
Strong knowledge of imitation learning, representation learning, and self-supervised learning across sensor modalities.
Proficiency with modern machine-learning frameworks and large-scale training infrastructure.
Fluent English is required; German is advantageous.
Ability to communicate effectively with researchers, engineers, and hardware teams.

Responsibilities

Model human behavior by transforming multimodal wearable data into representations that robots can learn from.
Address cross-embodiment transfer challenges by translating human movements and interactions into action spaces usable by robots with different body structures.
Design pretraining and fine-tuning strategies for robotic foundation models.
Build benchmarks that distinguish models that only imitate from models that generalize.
Translate model blind spots into precise requirements for data-capturing devices.
Collaborate closely with hardware and systems engineering teams while focusing on data and AI.

Desired Qualifications

German language proficiency.

Deine Mission & Herausforderungen

Menschliches Verhalten modellieren: Verwandle multimodale, am Körper getragene Daten in Repräsentationen, aus denen ein Roboter lernen kann.
Zwei Körper verbinden: Löse Cross-Embodiment-Transfer-Herausforderungen und übersetze menschliche Bewegungen und Interaktionen in Action Spaces, die ein Roboter mit einem völlig anderen Körperbau nutzen kann.
Foundation Models bauen, die handeln: Entwirf Pretraining- und Fine-Tuning-Strategien für robotische Foundation Models.
Den Beweis liefern: Baue die Benchmarks, die Modelle, die nur imitieren, von Modellen unterscheiden, die generalisieren.
Die nächste Datenerfassung mitgestalten: Verwandle blinde Flecken der Modelle in präzise, konkrete Anforderungen für Data-Capturing-Devices.
Cross-funktionale Zusammenarbeit: Arbeite Schulter an Schulter mit Hardware- und Systems-Engineering-Teams, während du dich klar auf die Daten- und KI-Seite fokussierst.

Was du mitbringst

Master- oder PhD-Abschluss in Computer Science, Machine Learning, Robotik oder vergleichbar.
Echte, praktische Erfahrung mit multimodalen Foundation Models (VLA, Video-Action oder Vergleichbares).
Erfahrung mit Cross-Embodiment-Transfer und RL-basiertem Retargeting.
Tiefes Verständnis von Low-Level Robotics Control.
Fundierte Kenntnisse in Imitation Learning, Representation Learning und Self-Supervised Learning über verschiedene Sensor-Modalitäten hinweg.
Sicherer Umgang mit modernen Machine-Learning-Frameworks und großskaliger Trainingsinfrastruktur.
Kommunikationsstärke, die dir den mühelosen Wechsel zwischen Researchern, Engineers und Hardware-Teams ermöglicht.
Fließendes Englisch, Deutsch von Vorteil.

About the Company

Neura Robotics

Cognitive robots with perception, manipulation, collaboration

Neura Robotics builds cognitive robots for industrial and service settings, combining perception, manipulation, and human collaboration. It develops both the hardware and the software stack for Physical AI, creating robot platforms like MAiRA, MAV, LARA and MiPA along with training infrastructure. The system works by integrating sensing, decision-making, and manipulation capabilities on a hardware-software stack that can be deployed in large-scale production, with the company planning robots fleets and NEURA Gyms for ongoing training and improvement. What sets Neura Robotics apart is its end-to-end approach to Physical AI, including a focus on mass production, an established global deployment plan, and a broad ecosystem of strategic backers and partners that support scaling and international expansion. The goal is to enable worldwide deployment of its robot platforms and associated training infrastructure, turning physical AI into widely available industrial and service automation.

Company size: 501-1,000
Company stage: Series C
Total funding: $1.7B
Headquarters: Metzingen, Germany
Founded: 2019

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