Research Scientist, Material Intelligence

Science is at the heart of everything we do at Google DeepMind. From the beginning, we took inspiration from science to build better algorithms, and now, we want to use our toolkit to accelerate scientific discovery. By bringing together specialists with backgrounds in machine learning, computer science, physics, chemistry, biology and more, we’re optimistic that we can build new methods that will push the boundaries of what is possible and help solve the biggest problems facing humanity.

Google DeepMind (GDM) is pursuing a ground-breaking research program in materials, aiming to accelerate the discovery of new functional materials by combining the predictive power of artificial intelligence (AI) and computational simulation with automated experimentation. The team is establishing experimental capacity to create a closed-loop, AI-driven discovery engine. Computational simulation is critical for grounding the AI and providing quick in silico feedback before materials are sent off to the lab for experimental validation.

We are seeking an exceptional and highly motivated expert in computational materials science, with broad expertise simulating diverse material classes, to help drive our in-silico discovery efforts. This is a senior position with a unique role blending scientific leadership, hands-on modeling, strategic input, and mentorship. You will be instrumental in guiding the computational team, supervising junior researchers, and refining the critical in-silico feedback loop that is at the heart of our mission.

• →Computational Leadership & Supervision: Lead and mentor a team of computational materials scientists, guiding project roadmaps, fostering scientific growth, and ensuring high-quality research output.
• →Modeling Strategy & Execution: Design and execute large-scale computational screening campaigns using DFT, molecular dynamics, and other simulation methods to predict novel materials with desired properties.
• →Broad Materials Expertise: Apply deep physical and chemical intuition across diverse material classes to identify promising avenues for discovery.
• →Method & Workflow Development: Review, integrate, and develop state-of-the-art computational tools and automated, high-throughput workflows on Google's large-scale compute infrastructure that can be tightly integrated with AI search methods.
• →Data Integrity & Feedback Loop: Ensure the generation of high-quality, reproducible computational data. Play a key role in structuring and curating simulation databases to train next-generation AI models.
• →Cross-functional Collaboration: Work closely with AI researchers and software engineers to translate AI-generated hypotheses into scalable simulation pipelines and to troubleshoot the simulation-to-reality gap.
• →Reporting & Communication: Clearly and efficiently report on computational progress, new material predictions, and challenges to the wider Material Intelligence team and key stakeholders.

• Significant post-PhD experience in Computational Materials Science, Solid-State Chemistry, Condensed Matter Physics, or a related field.
• Proven track record of supervising and mentoring junior computational researchers, postdocs, or students.
• Broad knowledge across multiple material classes and their relevant properties (e.g., electronic, magnetic, optical, mechanical).
• Deep, recognized expertise in first-principles simulation methods for materials (e.g., DFT, DFPT, MD) and a strong understanding of their application and limitations.
• Extensive hands-on experience using computational packages like VASP, Quantum ESPRESSO, LAMMPS, or similar.
• Strong programming skills (e.g., Python) for workflow management, data analysis, and tool automation.
• Demonstrated ability to independently lead and manage complex computational research projects, from conception to data analysis and communication.
• Excellent teamwork and communication skills, with proven experience in interdisciplinary collaboration, especially bridging the gap between computational/theory and experimental groups. 

• Experience in developing or applying machine learning models for materials property prediction (e.g., GNNs, ML-derived interatomic potentials).
• Expertise in high-throughput computational workflows and managing large-scale simulation campaigns on HPC or cloud infrastructure.
• A significant track record of high-impact research, reflected in publications, patents, or deployed technologies.
• Experience in strategic planning for a research group, including hiring and resource allocation.