Lila Sciences is looking to solve humankind's greatest challenges, such as human health, climate, and sustainability, by applying AI to every aspect of the scientific method and introducing scientific superintelligence.
Requirements
- Strong background in statistical mechanics, free energy calculations, reaction mapping, non-equilibrium dynamics, and rare-event sampling.
- Demonstrated expertise with molecular dynamics, Monte Carlo, and/or kinetic simulation software and frameworks (LAMMPS, GROMACS, OpenMM, HOOMD, etc.).
- Solid programming skills and experience with scientific computing (Python, C/C++, MPI, CUDA, etc.).
- Experience running and automating simulations on HPC and/or cloud environments at scale.
- Good familiarity with machine learning frameworks (PyTorch, JAX, TensorFlow, etc.)
- Prior experience working with machine learned interatomic potentials, including model training, fine-tuning, and data generation
Responsibilities
- Develop and extend molecular dynamics and Monte Carlo algorithms to capture rare events, non-equilibrium processes, transport phenomena, and mapping complex reaction networks.
- Build scalable simulation workflows that integrate statistical mechanics methods with machine learned interatomic potentials and agentic AI frameworks.
- Design methods for coupling dynamics simulations with experimental observables to enable closed-loop verification and discovery with automated labs.
- Collaborate with computational scientists, machine learning experts, and platform engineers to advance the fidelity and scalability of simulation-driven materials discovery.
- Establish reproducible, modular software pipelines for statistical mechanics and dynamics simulations that can be deployed on HPC and cloud-based infrastructure.
Other
- PhD or equivalent research/industry experience in Physics, Chemistry, Chemical Engineering, Mechanical Engineering, Applied Mathematics, or related fields.
- Strong publication record applying advanced statistical mechanics or dynamics simulations to molecular and materials systems.
- Prior work in coupling dynamics simulations with data-driven, AI-based, and/or agentic frameworks.
- Worked closely with experimental teams to extract and corroborate experimental observables from dynamics simulations
- Commitment 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.
