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Lila Sciences

AI-powered scientific discovery for medicines

Scientist 2/Senior Scientist - Solid-State Materials

Physical SciencesConfirmed live in the last 24 hours
Full-timeCambridge, MA, USAIn-person

About the Job

Requirements

  • A PhD in Solid-State Chemistry, Inorganic Chemistry, Materials Science, or a related field, with 1–5 years of postdoctoral or industry experience.
  • A documented portfolio of novel inorganic compounds spanning multiple chemical systems.
  • Hands-on proficiency across multiple inorganic synthesis modalities, with the ability to select an appropriate approach for a target phase.
  • Demonstrated experience performing materials synthesis campaigns driven by functional property targets, such as magnetism, nonlinear optical response, exotic charge transport, or related phenomena.
  • A 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, such as diffraction, magnetometry, transport, spectroscopy, and microscopy, to inform synthesis decisions and iteration.
  • Effective written and verbal communication, with a track record of cross-functional collaboration.

Responsibilities

  • Design and execute synthesis campaigns across multiple chemical systems and structure families, using solid-state preparative chemistry such as melt routes, flux, vapor transport, soft-chemical, topochemical, single-crystal growth, and unconventional or high-pressure environments as the target phase demands.
  • Translate phase-diagram, crystal-chemistry, and structure-property reasoning into 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, including powder X-ray diffraction, phase identification, and refinement, for materials identification and quality control.
  • Take synthesized materials through functional property screening, such as magnetic, transport, and optical screening, in partnership with the characterization team, and use resulting data to refine target selection and synthesis design.
  • Partner with materials simulation scientists, machine-learning researchers, and systems engineers to integrate synthesis outputs into closed-loop, artificial-intelligence-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 laboratory environment.
  • Maintain rigorous laboratory records and uphold safety and regulatory standards.

Desired Qualifications

  • Demonstrated depth in one or more functional inorganic material classes.
  • Experience with single-crystal growth, particularly metallic flux; additional methods such as vapor transport, Bridgman, and floating zone are welcome.
  • Experience with metallurgical processing techniques for densification and sample conditioning, such as sintering, hot pressing, spark plasma sintering, arc melting, and controlled annealing.
  • Experience with neutron scattering, pair distribution function, or other local-structure techniques for solving structure and ordering in disordered or metastable phases.
  • Comfort with inert-atmosphere handling, including glovebox and Schlenk techniques, and air- or moisture-sensitive chemistries.
  • Exposure to high-pressure or otherwise unconventional preparative environments.
  • Familiarity with thin-film or vacuum-based deposition techniques and how they fit alongside bulk synthesis.
  • Proficiency in Python or similar tools for data analysis and workflow automation.
  • Exposure to automated, high-throughput, or autonomous laboratory environments.

About the Company

Lila Sciences

AI-powered scientific discovery for medicines

Lila.ai uses artificial intelligence to speed up scientific discovery in medicines and advanced materials. The system ingests large sets of scientific data, experimental results, and literature to identify patterns, generate hypotheses, and guide research programs. It differentiates itself by tackling deep scientific problems at scale and combining AI with domain expertise, rather than offering generic tools. The goal is to help humanity by delivering new medicines and materials that solve major biological and materials challenges through data-driven discovery.

Company size
501-1,000
Company stage
Series A
Total funding
$550M
Headquarters
Cambridge, MA
Founded
2024

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