Research Scientist, Photonic Materials Discovery

Your Impact at LILA We are seeking a computational materials scientist to discover and optimize 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—and translating those insights into experimentally testable materials 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 such as electronic structure, dielectric and optical response, polarization, phonons, and electro-optic coefficients. You will also 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. This is a hands-on scientific role at the intersection of condensed-matter physics, materials chemistry, photonics, and AI-enabled discovery. You will collaborate with experimental scientists, ML researchers, and software engineers to build validated workflows, establish structure–property–performance relationships, and prioritize candidates for experimental evaluation. What You'll Be Building Lead computational discovery efforts for materials relevant to electro-optic and integrated photonic applications. Develop and validate first-principles, atomistic, and multiscale workflows—including DFT and response-property calculations—to predict electronic, vibrational, dielectric, optical, and electro-optic behavior. Interpret material response across composition, structure, defects, interfaces, strain, and temperature; assess stability, synthesizability, and performance tradeoffs to prioritize candidates. Connect intrinsic material properties to device requirements such as optical loss, modulation efficiency, operating wavelength, and fabrication compatibility. Compare predictions with experimental measurements, investigate discrepancies, and build effective computational–experimental feedback loops. Build reproducible, automated workflows for high-throughput simulation, data provenance, validation, convergence testing, and uncertainty assessment. Develop agentic frameworks that orchestrate simulation codes, scientific databases, analysis tools, and surrogate models; partner with ML and software teams on planning, validation, failure recovery, and human review. Analyze simulation and experimental data to generate actionable materials hypotheses and communicate recommendations, assumptions, and limitations. What You'll Need to Succeed PhD or equivalent experience in Physics, Materials Science, Chemistry, Electrical Engineering, or a related field. Strong background in computational condensed-matter physics, materials science, physical chemistry, or a related discipline, with experience studying functional optical, dielectric, or electronic materials. Expertise in electronic-structure methods and calculating and interpreting dielectric, optical, vibrational, polarization, or related response properties using perturbative, finite-field, Berry-phase, or comparable methods. Working knowledge of crystallographic symmetry, electronic structure, lattice dynamics, light–matter interaction, and structure–property relationships. Experience with established electronic-structure packages and reproducible HPC or cloud workflows, including scheduling, data management, and automated analysis; strong Python and scientific software skills. Familiarity with agentic AI, tool-calling, or workflow orchestration and the design of reliable, auditable workflows across scientific tools. Bonus Points For Experience with materials or device concepts relevant to electro-optics and integrated photonics, including ferroelectrics, semiconductors, oxides, nitrides, chalcogenides, or low-dimensional materials. Familiarity with advanced electronic-structure, excited-state, finite-temperature, or multiscale methods—such as hybrid-functional, many-body, molecular-dynamics, or effective-Hamiltonian approaches—when standard DFT is insufficient. Experience modeling defects, surfaces, interfaces, thin films, strain, or other non-ideal effects, and connecting atomistic predictions to electromagnetic, device, or process models. Experience building high-throughput workflows, materials data systems, surrogate models, or active-learning loops, including applications of AI/ML to computational materials science or physics-based simulation. Hands-on experience with agentic or tool-using systems and orchestration patterns for long-running scientific tasks, including branching, retries, checkpointing, and asynchronous execution. Experience designing evaluation, observability, error recovery, provenance, and human oversight for agen