Team Lead AI Engineering (Robotics)

Location: Aachen, Germany

Company

hirify.global develops physical AI systems and robotic capabilities through its NEURA Gym network of physical training centers.

What you will do

• Hire, coach, and lead AI Engineers building deployable robotic skills.
• Own the technical relationship with NEURA Gym partners, evaluating use cases, feasibility, algorithms, and data collection strategies.
• Oversee the end-to-end pipeline from teleoperation and data collection through training, simulation, deployment, and validation.
• Work with Robotics Engineers during live data collection and experiments while maintaining high safety standards.
• Provide hands-on support with training pipelines, data quality investigations, and model-related bugs.
• Connect NEURA Gym, Cloud, and Core AI teams and use deployment learnings to improve the product roadmap and core AI capabilities.

Requirements

• Degree in Computer Science, Robotics, Electrical Engineering, or a related field, or equivalent practical experience.
• Hands-on experience developing machine learning solutions for robotics, with expertise in at least two of vision-based manipulation, reinforcement or imitation learning, and Vision-Language-Action or multimodal foundation models.
• Experience leading engineers, projects, or technical initiatives.
• Practical experience with PyTorch or TensorFlow and strong Python programming skills.
• Confidence working with physical robotic systems and robotics software stacks and middleware.
• Professional working proficiency in English is required.

Nice to have

• C++ experience.
• Familiarity with AWS, Azure, or GCP.
• Experience with Isaac Sim or MuJoCo.
• German language skills.

Culture & Benefits

• Work on AI systems deployed on physical robots rather than research prototypes.
• Collaborate directly with customers and end-users to bring robotic applications to life.
• Work across AI engineering, robotics, cloud, and core AI teams.
• Drive measurable improvements through metrics such as skill success rate, time-to-validated skill, and data efficiency.