Research Scientist I/II, Statistical Mechanics and Dynamics

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

Offered From the posting source checked Jun 20, 2026

Verification

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Salary

$176K-$234K From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

76% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Research Scientist I/II, Statistical Mechanics and Dynamics Cambridge, MA USA Your Impact at LILA 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. What You'll Be Building - 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. What You'll Need to Succeed - 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,

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