YOUR MISSION & CHALLENGES

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

WHAT WE CAN LOOK FORWARD TO

• 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.