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Salary: USD 176,000 - 304,000 / annual
Lila Sciences is seeking a Research Scientist to apply computational condensed matter physics and electronic structure expertise to accelerate materials discovery and optimization. You will use first-principles modeling, atomistic simulations, scientific machine learning, and agentic AI systems to investigate complex materials relevant to superconductors, quantum materials, and electronic devices.
Your work will sit at the intersection of physics-based simulation, AI/ML, and autonomous scientific workflows. You'll use computational insights to identify promising materials, explain structure-property relationships, guide optimization, and help AI agents reason over simulation and experimental data in scientifically grounded ways.
Key responsibilities include:
- Apply computational condensed matter physics methods (electronic structure, phonon calculations) to study quantum materials, superconductors, and electronic devices
- Connect simulation outputs to experimental observations and develop closed-loop workflows between computation and experiment
- Build predictive models from computational and experimental data to guide materials selection and optimization
- Analyze simulation and experimental data to generate actionable materials hypotheses
- Partner with ML, software, and experimental teams on discovery workflows
- Communicate physical insights, model limitations, and recommendations to cross-functional collaborators
Required qualifications:
- PhD or equivalent in Physics, Materials Science, Chemistry, Applied Mathematics, or related field
- Strong foundation in computational condensed matter physics, electronic structure, or atomistic simulation
- Deep understanding of electronic-structure theory (DFT, beyond-DFT methods, quantum chemistry)
- Experience applying first-principles or atomistic methods to materials discovery
- Familiarity with superconductors, quantum materials, semiconductors, or device-relevant systems
- Strong Python programming and scientific computing skills
Bonus experience includes work with amorphous materials, vibrational properties, advanced electronic structure methods, AI/ML applied to materials science, agentic AI systems, autonomous workflows, and closed-loop optimization integrating computation with experimental characterization.
