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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.
Responsibilities
- 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.
Requirements
- 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 integrate experiments, simulations, and models.
- Track record of setting and influencing a research agenda — defining thesis, prioritizing under uncertainty, and making irreversible bets.
- Strong intuition for physical systems, chemistry, and their constraints.
- Excellent cross-disciplinary communication skills; ability to influence technical and non-technical stakeholders and external collaborators.
- Fluency in English; additional languages are a plus.
- Eligibility to work in Germany without employer visa sponsorship.
Skills
- physics-informed machine learning
- representation learning
- foundation models
- materials science
- physical domains
- scientific ML
- equivariant neural networks
- generative models for molecules/materials
- multi-fidelity modeling
- automated laboratory environments
- energy materials landscape
- batteries
- catalysts
- photovoltaics
Location
- Berlin, Germany
Work Type
- On-site
- Full-time
Experience Level
- Senior leadership
- 7+ years of machine learning research experience
Education Level
- PhD strongly preferred
- Advanced degree