Lila Sciences is looking to solve humankind's greatest challenges in human health, climate, and sustainability by building a scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.
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.
- Prior work in coupling dynamics simulations with data-driven, AI-based, and/or agentic frameworks.
- 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.
- Worked closely with experimental teams to extract and corroborate experimental observables from dynamics simulations
- We are uniquely cross-functional and collaborative.
- We are actively reimagining the way teams work together and communicate.
- Therefore, we seek individuals with an inclusive mindset and a diversity of thought.
