Head of AI Research

Job description

About the Role We are a seed-stage deeptech startup building an advanced materials acceleration platform that combines physics-informed AI, robotics, and real-world experimental data to dramatically shorten the timeline for discovering new materials — particularly for the energy sector. We are seeking a Head of AI Research to define and lead our AI-for-Materials research agenda. This is a senior leadership role sitting at the intersection of machine learning, physics, chemistry, and automated experimentation. You will shape the scientific vision that translates cutting-edge research insight into reliable, end-to-end discovery pipelines — and build the world-class team to execute it. Location: Berlin, Germany (on-site). Visa sponsorship is not available — candidates must be eligible to work in Germany without employer sponsorship. What You'll Do Define and champion the research thesis for AI-native materials discovery; set high-impact research bets and long-horizon strategy. Identify where existing ML paradigms fall short for physical matter and specify what needs to be invented instead. Lead development of a Materials World Model that bridges experiments, simulations, and learned representations. Collaborate closely with Programs, Hardware & Automation, and Software Architecture teams to embed research into end-to-end autonomous discovery loops. Ensure models stay grounded in physical reality and experimental feedback — not just abstract data. Balance ambitious long-horizon research goals with near-term deliverables and milestones. Build, hire, mentor, and challenge a multi-disciplinary team of senior ML researchers and scientists. Foster a culture of deep thinking, honest evaluation, scientific taste, and intellectual courage. Influence the broader scientific community through collaborations and a clear, opinionated point of view on future directions. What We're Looking For Required: Advanced degree (PhD strongly preferred) in machine learning, physics, chemistry, or a closely related field. 7+ years of machine learning research experience, including demonstrated leadership of senior individual contributors or head-of-team responsibility. Deep expertise in physics-informed machine learning , representation learning, or foundation models applied to materials science or related physical domains. Proven ability to build end-to-end discovery pipelines that int…