Your mission
We are seeking hands-on Research Scientists to execute day-to-day experimental synthesis and characterization of AI-predicted materials. You will work directly with the Head of Materials Synthesis & Characterization and computational team to rapidly translate AI materials predictions into physical samples and high-quality characterization data. This is a lab-first, execution-driven role with strong ownership of experimental results and direct contribution to cutting edge AI-enable workflow and feedback loop, IP generation and industrial validation.
We look for ambitious candidates with passion and perseverance, and for good team players. The ideal candidate is an independent problem solver, someone who likes to make new things, and has the grit necessary to make things work. You will be part of an agile and growing team, pushing boundaries for progress and discoveries with a competitive compensation package including equity participation.
You should have a material synthesis expert with a background in condensed matter physics, inorganic chemistry, or material science. You should have expertise in a few of the following synthesis techniques: physical vapor deposition, melt-growth, flux-growth, and arc-melting. You should be familiar with X-ray diffraction and spectroscopy, electron microscopy, and elemental analysis, as well as PPMS (or similar) methods. You should have a high level of experimental rigor and documentation discipline.
Your profile
Required Qualifications
- PhD or MSc in physics, chemistry, or material science with 5+ years of hands-on experience
- Expertise in the synthesis of inorganic crystals, e.g. permanent magnets, van der Waals materials, superconductors, Perovskite compounds, Kondo topological fermions, cubic and hexagonal Laves compounds, etc.
- Expertise in characterization of new materials using X-ray structure determination, SEM-EDS
- Expertise in characterization of new materials using cryogenic transport and magnetization measurements (e.g., PPMS, VSM)
- Ability to quickly learn different growth methods
Preferred Qualifications
- Knowledge of different low-temperature materials characterization methods
- Experience with magnets
- Familiarity with automation and lab data management
- Experience in generating data for patents or industrial qualifications
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