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Salary: USD 126,000 - 198,000 / annual
Lila Sciences is building Scientific Superintelligence to solve major challenges by combining advanced AI models with proprietary AI Science Factory instruments. As a Scientist II/Senior Scientist on the Vacuum Synthesis team, you will advance the design and synthesis of functional inorganic solid-state materials in support of Lila's autonomous science platform.
You will design and execute multi-step synthesis campaigns across diverse chemical systems, translating phase-diagram and crystal-chemistry reasoning into prioritized synthesis pathways and experimental feedback. Your responsibilities include:
• Design and execute synthesis campaigns across multiple chemical systems and structure families, drawing on solid-state preparative chemistry (melt routes, flux, vapor transport, soft-chemical, topochemical, single-crystal growth, and high-pressure environments).
• Translate phase-diagram, crystal-chemistry, and structure-property reasoning into concrete, prioritized synthesis pathways for novel functional inorganic compounds.
• Access metastable and kinetically trapped phases through appropriate low-temperature and soft-chemical routes.
• Work with the characterization team to develop routine and specialized structural characterization workflows (powder XRD, phase ID, refinement) for materials identification and quality control.
• Take synthesized materials through functional property screening (magnetic, transport, optical, or related) and use resulting data to refine target selection and synthesis design.
• Partner with materials simulation scientists, ML researchers, and systems engineers to integrate synthesis outputs into closed-loop, AI-guided experimental workflows.
• Develop and document robust protocols for air- and moisture-sensitive precursors, controlled-atmosphere processing, and post-synthetic workup in a highly automated lab environment.
• Maintain rigorous laboratory records and uphold safety and regulatory standards.
You will work cross-functionally with synthetic materials scientists, characterization scientists, ML researchers, and systems engineers to close the synthesis-characterization-modeling loop and accelerate the path to scientific superintelligence in functional materials.
REQUIREMENTS:
• PhD in Solid-State Chemistry, Inorganic Chemistry, Materials Science, or related field, with 1–5 years of postdoctoral or industry experience.
• Documented portfolio of novel inorganic compounds spanning multiple chemical systems.
• Hands-on proficiency across multiple inorganic synthesis modalities, with comfort selecting the right approach for a given target phase.
• Demonstrated experience performing materials synthesis campaigns driven by functional property targets (e.g., magnetism, nonlinear optical response, exotic charge transport).
• Strong working command of complex multicomponent and multidimensional phase diagrams and structural-chemistry principles governing phase formation and stability.
• Deep proficiency in X-ray diffraction and scattering techniques, including quantitative phase analysis and Rietveld refinement.
• Fluency working across multimodal characterization (diffraction, magnetometry, transport, spectroscopy, microscopy) to inform synthesis decisions and iteration.
• Effective written and verbal communication with a track record of cross-functional collaboration.
BONUS QUALIFICATIONS:
• Demonstrated depth in one or more functional inorganic material classes.
• Experience with single-crystal growth, particularly metallic flux, with additional methods (vapor transport, Bridgman, floating zone).
• Experience with metallurgical processing techniques (sintering, hot pressing, spark plasma sintering, arc melting, controlled annealing).
• Experience with neutron scattering, pair distribution function (PDF), or other local-structure techniques.
• Comfort with inert-atmosphere handling (glovebox, Schlenk) and air- or moisture-sensitive chemistries.
• Exposure to high-pressure or unconventional preparative environments.
• Familiarity with thin-film or vacuum-based deposition techniques.
• Proficiency in Python or similar tools for data analysis and workflow automation.
• Exposure to automated, high-throughput, or autonomous laboratory environments.
