NEURA is scaling its AI department to execute on our product roadmap. As Group Lead AI Engineering, you own a focused sub-team within the AI department. You set technical direction, make critical architectural decisions, and unblock your engineers, while remaining hands-on enough to review work, challenge approaches, and demonstrate what great looks like.
Own the technical roadmap of your sub-team: define goals, break them into executable engineering tracks, and ensure alignment with the broader AI and product strategy.
Lead and grow a high-performance team of AI engineers: recruit top talent, conduct technical interviews, set performance expectations, and develop individual contributors into senior engineers.
Drive research-to-production transitions: evaluate state-of-the-art methods, assess feasibility, and build the engineering bridges that get AI models running reliably on real robot hardware.
Collaborate cross-functionally with Software, Hardware and Product Management to align AI capabilities with platform requirements and customer use cases.
Define and track engineering quality: establish testing standards, model evaluation pipelines, deployment metrics, and data governance practices for your domain.
Step in hands-on when your team hits hard blockers: review training runs, debug model behavior, challenge architectural assumptions, and lead by example in solving hard problems.
Aggregate lessons learned and feed them back into the core AI roadmap
An excellent Master's or PhD in Computer Science, Robotics, Electrical Engineering, or a related field
7+ years of hands-on ML or robotics AI engineering, with at least 2 years in a formal or informal technical lead role. Production systems experience is essential.
Strong command of at least two of: vision-based perception, manipulation & control, reinforcement / imitation learning, multimodal foundation models, or scalable MLOps.
Strong Python (C++ is a plus); practical PyTorch or JAX experience; familiarity with cloud infrastructure (AWS, GCP, or Azure) and CI/CD for ML systems.
Comfort working with real robot hardware and simulation environments (IsaacSim, MuJoCo, or equivalent). You understand that real-world deployment is the only true test.
Proven ability to set technical direction, mentor engineers, conduct code and design reviews, and make decisions under uncertainty.
You translate between researchers, product managers, and hardware engineers without losing precision. Professional English required; German is a strong plus (B2–C1).