Materials Laboratory Scientist, Science, DeepMind
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MINIMUM QUALIFICATIONS:
- PhD in Materials Science, Physics, Chemistry, Chemical Engineering, or a related science discipline, or equivalent practical.
- 2 years of experience with laboratory synthesis, preparation, or characterization of materials.
- Experience working in cross-functional or interdisciplinary technical/scientific teams (e.g., across engineering, computing, materials science, or chemistry).
- Experience troubleshooting, root-cause diagnosing, and resolving technical or experimental bottlenecks in a scientific research or laboratory environment.
- Submission of scientific publications, conference proceedings, patents, or technical white papers in Materials Science, Physics, or a related discipline.
PREFERRED QUALIFICATIONS:
- Experience working with high-throughput experimental platforms, laboratory automation, or robotic instrumentation.
- Proficiency in programming (e.g., Python) for scientific data analysis, automation scripting, or instrument interfacing.
- Foundational understanding of computational materials science or exposure to machine learning workflows in physical sciences.
- Familiarity with Laboratory Information Management Systems (LIMS), electronic lab notebooks, or structured research data pipelines.
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ABOUT THE JOB:
We are seeking an experimental Materials Laboratory Scientist to join our team in London. In this role, you will work on experimental realization, processing, and characterization of novel materials predicted by our generative AI models. You will drive experimental workflows across laboratory synthesis and characterization, bridging traditional physical sciences with data-driven research. Working side-by-side with AI researchers, software engineers, and automation roboticists, you will help scale laboratory throughput, build automated feedback loops, and accelerate materials discovery.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
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RESPONSIBILITIES:
- Conduct structural, compositional, and physical property characterization across a variety of metrology platforms to validate synthesized materials.
- Provide systematic experimental feedback and guide active learning pipelines in close collaboration with computational and machine learning researchers.
- Translate benchtop protocols into high-throughput automated workflows in partnership with automation engineers.
- Execute experimental synthesis and processing workflows across inorganic, solid-state, thin-film, or other chemical routes to realize target materials candidates.
- Maintain thorough data hygiene, capturing comprehensive experimental telemetry and protocols within digital laboratory systems (ELN/LIMS) to support machine learning datasets.
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