Materials Laboratory Scientist, Science, DeepMind

Google; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

Google is seeking an experimental Materials Laboratory Scientist to join our London team. You will drive experimental realization, processing, and characterization of novel materials predicted by generative AI models, bridging traditional physical sciences with data-driven research.

You will collaborate with AI researchers, software engineers, and automation experts to scale throughput, build automated feedback loops, and accelerate materials discovery while maintaining rigorous data hygiene in

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.
• Experience troubleshooting, root-cause diagnosing, and resolving bottlenecks in a scientific lab environment.
• Publications, conference proceedings, patents, or technical white papers in Materials Science, Physics, or related fields.

Responsibilities

• Conduct structural, compositional, and physical property characterization across metrology platforms to validate synthesized materials.
• Provide systematic experimental feedback and guide active learning pipelines with computational/ML researchers.
• Translate benchtop protocols into high-throughput automated workflows with automation engineers.
• Execute experimental synthesis and processing across inorganic, solid-state, thin-film, or other chemical routes to realize target materials candidates.
• Maintain thorough data hygiene, capturing experimental telemetry and protocols within ELN/LIMS for ML datasets.

Skills

Materials synthesis

Characterization techniques

Interdisciplinary collaboration

Lab troubleshooting

Scientific publications

Education

PhD in Materials Science/Physics/Chemistry/Engineering

Tools

LIMS/ELN

High-throughput platforms

Python for data analysis

Job description

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.

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.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

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.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.