Research Scientist I/II, Statistical Mechanics and Dynamics

Lila Sciences•Cambridge, MA

9d•$176,000 - $234,000

About The Position

Your role in our Physical Sciences division will focus on designing and implementing state-of-the-art simulation approaches to model transport, kinetics, rare events, and reaction networks, and integrating them with AI-driven platforms for materials discovery. Your work will be integral to our efforts in predicting, designing, and controlling the behavior of complex materials and molecular systems, and their acceleration via agentic AI. You will partner with diverse teams at Lila, including machine learning experts working on scientific superintelligence and materials science experts performing real-world experiments.

Requirements

PhD or equivalent research/industry experience in Physics, Chemistry, Chemical Engineering, Mechanical Engineering, Applied Mathematics, or related fields.
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.

Nice To Haves

Strong publication record applying advanced statistical mechanics or dynamics simulations to molecular and materials systems, including but not limited to molecular/biomolecular systems and solid-state materials and interfaces.
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
Worked closely with experimental teams to extract and corroborate experimental observables from dynamics simulations

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.