Research Scientist, Computational Condensed Matter Physics

1 мес. назад

USASenior

electronic structurephonon calculationsmachine learningscientific computing

Apply computational condensed matter physics and electronic structure expertise to accelerate materials discovery and optimization using first-principles modeling, atomistic simulations, scientific machine learning, and AI.

Требования

PhD or equivalent experience in Physics, Materials Science, Chemistry, Applied Mathematics, or a related field.
Strong foundation in computational condensed matter physics, electronic structure, or atomistic simulation.
Deep understanding of electronic-structure theory (quantum chemistry, DFT, beyond-DFT methods, etc.) and their application to electronic, magnetic, or quantum materials.
Experience applying first-principles or atomistic methods to materials discovery, optimization, or understanding.
Familiarity with superconductors, quantum materials, electronic materials, semiconductors, or device-relevant materials systems.
Strong programming skills in Python and scientific computing workflows.

Будет плюсом

Experience working with amorphous materials, vibrational properties calculations and advanced electronic structure methods.
Experience applying AI/ML to computational materials science or physics-based simulation data.
Familiarity with agentic AI systems, autonomous scientific workflows, or simulation-aware agents.
Background working with quantum materials, superconductors, semiconductors, or electronic device materials.
Experience integrating computational predictions with experimental characterization, device measurements, or closed-loop optimization workflows.
Ability to communicate physical insight, uncertainty, and model limitations to cross-functional collaborators.

Условия

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.

Другое

Your role will involve applying computational condensed matter physics and electronic structure expertise to accelerate materials discovery, optimization, and understanding. You will use first-principles modeling, atomistic simulations, scientific machine learning, and agentic AI systems to investigate complex materials with direct relevance to superconductors, quantum materials, or electronic devices.
You will work at the intersection of physics-based simulation, AI/ML, and autonomous scientific workflows. The focus is on using computational insight to identify promising materials, explain structure-property relationships, guide optimization, and help agents reason over simulation and experimental data in scientifically grounded ways.
This is a hands-on research role for someone who can connect deep condensed matter and electronic structure expertise with practical materials discovery impact. You will collaborate with computational scientists, AI researchers, software engineers, and experimental teams to turn simulations, models, and scientific reasoning into actionable hypotheses and discovery workflows.
Apply computational condensed matter physics to materials discovery and optimization.
Use computational physics methods (such as electronic structure and phonon calculations) to study quantum materials, superconductors and electronic devices.
Connect simulation outputs to experimental observations and develop workflows that close the loop 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 collaborators.
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy .
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.