NEURA is scaling its AI department to implement our product roadmap. As Group Lead AI Engineering, you will be responsible for a focused sub-team within the AI organization. You will define the technical direction, make crucial architectural decisions, and support your team in overcoming challenges—while remaining close enough to the technology to review work, critically examine approaches, and lead by example in what excellent engineering looks like.
Take responsibility for your team's technical roadmap : define goals, translate them into actionable engineering streams, and ensure alignment with the overarching AI and product strategy.
Lead and develop a high-performing team of AI Engineers : attract top talent, conduct technical interviews, set clear expectations, and develop employees into Senior Engineers.
Drive the transfer from research to production : evaluate state-of-the-art methods, test feasibility, and build the technical bridges that reliably allow AI models to run on real robotics hardware.
Work cross-functionally with software, hardware, and product teams to align AI capabilities with platform requirements and customer use cases.
Define and monitor engineering quality standards : testing standards, model evaluation pipelines, deployment metrics, and data governance practices within your area of responsibility.
Take a hands-on approach when your team encounters complex blockers: review training runs, debug model behavior, question architectural decisions, and lead by technical example.
Gather learnings from projects and actively feed them back into the Core AI Roadmap .
An outstanding Master's or PhD degree in Computer Science, Robotics, Electrical Engineering or a related field.
7+ years of practical experience in ML or Robotics AI engineering, including at least 2 years in a formal or informal technical lead role . Experience with production systems is essential.
Solid expertise in at least two of the following areas:
Vision-based Perception
Manipulation & Control
Reinforcement / Imitation Learning
Multimodale Foundation Models
Scalable MLOps systems
Excellent Python skills (C++ is a plus), practical experience with PyTorch or JAX , and knowledge of cloud infrastructure (AWS, GCP or Azure) and CI/CD for ML systems .
Enjoy working with real robotics hardware and simulation environments like IsaacSim, MuJoCo , or similar tools. You know: The real test always takes place in the real world.
Demonstrable ability to provide technical direction , coach engineers, conduct code and design reviews, and make decisions under uncertainty.
You communicate effectively between research, product management and hardware engineering , without losing technical precision.
Fluent English is required; German language skills (B2–C1) are a strong plus.