Research Scientist, Computational Condensed Matter Physics
$176k - $304k • Cambridge, MA USA • Full-time
Posted 3mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Engineer
About the job
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges by leveraging AI to accelerate scientific discovery. This role focuses on applying computational condensed matter physics and electronic structure expertise to materials discovery, optimization, and understanding. You will utilize first-principles modeling, atomistic simulations, scientific machine learning, and agentic AI systems to investigate complex materials relevant to superconductors, quantum materials, or electronic devices. The work involves a blend of physics-based simulation, AI/ML, and autonomous scientific workflows, aiming to generate actionable hypotheses and discovery workflows by reasoning over simulation and experimental data.
Responsibilities
- Apply computational condensed matter physics to materials discovery and optimization.
- Use computational physics methods (e.g., electronic structure, phonon calculations) to study quantum materials, superconductors, and electronic devices.
- Connect simulation outputs to experimental observations and develop workflows that integrate 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.
- Collaborate with ML, software, and experimental teams on discovery workflows.
- Communicate physical insights, model limitations, and recommendations to collaborators.
Requirements
- 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 (e.g., quantum chemistry, DFT) and its application to 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.
Benefits
- Competitive base compensation with bonus potential
- Generous early-stage equity
- Medical, dental, and vision coverage
- Employer-paid life and disability insurance
- Flexible time off with generous company wide holidays
- Paid parental leave
- Educational assistance program
- Commuter benefits (including bike share memberships for office-based employees)
- Company subsidized lunch program