Building the Gyms Where Physical AI Learns to Work
Physical AI can't be trained on the internet. It has to be trained in the real world - through seeing, hearing and feeling real tasks in real environments. That's what NEURA Gym is about: a global network of physical training centers where robots learn industrial skills directly from the people who've mastered them.
As an (Senior) Robotics AI Engineer – Training & Deployment (Physical AI), you will play a key role in enabling robots to learn new skills through data collection, model training, and real-world testing. Working at the intersection of robotics, AI, and user enablement, you will guide customers and internal stakeholders through the complete RoboGym training pipeline, helping transform robotic learning challenges into successful deployments.
Your mission & challenges
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Serve as a hands-on trainer and technical advisor for RoboGym users, designing training curricula, assessing task feasibility, and planning demonstration and data collection sessions for robot skill acquisition.
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Onboard and guide users through the complete NeuraGym workflow, including teleoperation, data annotation, model training, simulation, deployment, and validation.
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Collaborate closely with robotics engineers during data collection and live demonstration sessions to ensure desired robot behaviors and safety standards are achieved.
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Guide users through model training and refinement by reviewing training scripts, identifying data quality issues, helping reproduce and resolve learning failures, and providing hands-on support when persistent blockers arise.
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Act as a trusted interface between users and the Cloud and AI teams, fostering collaboration, facilitating knowledge transfer, and ensuring timely support when needed.
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Capture lessons learned from training engagements and feed insights back into both the NeuraGym product roadmap and the Neura Core AI roadmap.
What we can look forward to
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Master's degree in Computer Science, Robotics, Electrical Engineering, Artificial Intelligence, or a related technical field, or comparable qualifications and practical experience.
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Proven hands-on experience in robot learning, Physical AI, or embodied AI, for example through work with Vision-Language-Action (VLA) models, video-action models or similar.
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Hands-on experience working with robotic hardware, teleoperation systems, and associated middleware in real-world environments.
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Experience with cloud platforms (AWS, Azure, or GCP) and robotics simulation environments such as NVIDIA Isaac Sim, MuJoCo, or similar tools is a plus.
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Strong instructional and problem-solving skills, with the ability to manage multiple training projects in parallel.
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Strong communication skills with the ability to effectively bridge the gap between researchers, engineers, and end users
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Professional proficiency in English, German language skills are beneficial.
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