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Lila is seeking a computational materials scientist to lead discovery and optimization of materials for electro-optic and photonic technologies. The role centers on understanding how composition, structure, defects, processing conditions, and operating environments influence optical and electro-optic behavior, then translating those insights into experimentally testable hypotheses.
You will develop first-principles and multiscale simulation workflows spanning electronic-structure calculations, lattice dynamics, atomistic modeling, and connections to electromagnetic or device-level models. These workflows will predict properties including electronic structure, dielectric and optical response, polarization, phonons, and electro-optic coefficients. You will integrate these capabilities into automated, agentic discovery systems that can plan studies, select and invoke tools, evaluate results, recover from failures, and iteratively refine computational hypotheses.
Key responsibilities include leading computational discovery efforts for electro-optic and integrated photonic applications; developing and validating first-principles, atomistic, and multiscale workflows including DFT and response-property calculations; interpreting material response across composition, structure, defects, interfaces, strain, and temperature; connecting intrinsic material properties to device requirements such as optical loss, modulation efficiency, and fabrication compatibility; comparing predictions with experimental measurements and building effective feedback loops; building reproducible, automated workflows for high-throughput simulation with data provenance and validation; developing agentic frameworks that orchestrate simulation codes, scientific databases, analysis tools, and surrogate models; and analyzing data to generate actionable materials hypotheses.
Required qualifications include a PhD or equivalent in Physics, Materials Science, Chemistry, Electrical Engineering, or related field; strong background in computational condensed-matter physics or materials science with experience studying functional optical, dielectric, or electronic materials; expertise in electronic-structure methods and calculating response properties using perturbative, finite-field, or Berry-phase methods; working knowledge of crystallographic symmetry, electronic structure, lattice dynamics, and structure-property relationships; experience with electronic-structure packages and reproducible HPC or cloud workflows; and strong Python and scientific software skills. Familiarity with agentic AI, tool-calling, and workflow orchestration is required.
Bonus qualifications include experience with materials relevant to electro-optics and integrated photonics (ferroelectrics, semiconductors, oxides, nitrides, chalcogenides); advanced electronic-structure methods (hybrid-functional, many-body, molecular-dynamics); modeling defects, surfaces, interfaces, and strain; building high-throughput workflows and materials data systems; hands-on experience with agentic systems and orchestration patterns; and ability to communicate physical insight and model limitations to cross-functional teams.
