Materials Laboratory Scientist, Science, DeepMind

Overview

In this role you drive experimental realization and characterization of novel materials guided by generative AI models. You will bridge traditional physical sciences with data-driven research, working with AI researchers, software engineers, and automation experts to scale throughput and build automated feedback loops. You translate benchtop protocols into high-throughput workflows and maintain rigorous data hygiene to support ML datasets. This position offers the chance to push materials discovery at a leading AI lab, with impact on scientific discovery and product innovation.

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 computations and 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 complete experimental telemetry and protocols within ELN/LIMS to support ML datasets

Key requirements

• PhD in Materials Science, Physics, Chemistry, Chemical Engineering, or related science discipline, or equivalent practical experience
• 2 years of experience with laboratory synthesis, preparation, or characterization of materials
• Experience in cross-functional or interdisciplinary technical teams
• Experience troubleshooting, root-cause diagnosing, and resolving technical bottlenecks in a scientific lab environment
• Publication, conference proceeding, patent, or technical white paper in Materials Science, Physics, or related discipline

• cross-functional collaboration
• problem-solving and troubleshooting
• effective scientific communication
• Experience with high-throughput experimental platforms
• Laboratory automation or robotic instrumentation
• Proficiency in Python for data analysis, automation scripting, or instrument interfacing

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