LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
Lila Sciences is seeking a Computational Scientist I/II, Soft Matter Formulations , Solids and Melts to develop models, tools, and workflows that accelerate discovery across polymeric and soft material systems. This role focuses on solids and viscoelastic materials, including polymers and elastomers, gels, hot-melt adhesives, composites, powders, films, semi-solids, and crystalline or amorphous solids.
What they're looking for
- Experience applying machine learning to scientific, materials-focused, polymer, soft matter, or formulation problems
- Domain expertise in polymer science, elastomers, gels, adhesives, composites, rheology, solid materials, complex fluids, or related fields
- Familiarity with mechanical, thermal, morphological, or processing-sensitive material properties
- Strong Python skills and experience with modern ML frameworks
- Experience training, evaluating, and improving models using experimental, simulation, or scientific datasets
- Ability to use simulations, theory, descriptors, or mechanistic understanding to inform modeling choices for polymer and soft material systems
More about this role
Lila Sciences is seeking a Computational Scientist I/II, Soft Matter Formulations , Solids and Melts to develop models, tools, and workflows that accelerate discovery across polymeric and soft material systems. This role focuses on solids and viscoelastic materials, including polymers and elastomers, gels, hot-melt adhesives, composites, powders, films, semi-solids, and crystalline or amorphous solids.
You will bring domain expertise in polymer science, soft matter physics, rheology, solid materials, formulation science, or a closely related area, and apply machine learning methods to connect formulation choices, processing history, structure, morphology, and end-use performance. The work spans melt processing, mechanical performance, thermal transitions, processing windows, crystallinity, cross-link density, cure kinetics, and formulation-to-processing-to-property relationships.
This is a hands-on scientific ML role for someone who can bridge domain context and computational execution. You will develop structure-property models for solid and viscoelastic materials, build cure- and processing-aware representations, incorporate molecular or polymer descriptors and simulation...
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