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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