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 - Complex Fluids to develop models, tools, and workflows that accelerate discovery across liquid and flowable soft material systems. This role focuses on complex fluids, including colloidal suspensions, emulsions, surfactant systems, polymer solutions, coolants and heat-transfer fluids, coatings, inks, and lubricants.
What they're looking for
- Experience applying machine learning to scientific, materials-focused, complex fluid, soft matter, or formulation problems
- Domain expertise in colloids, emulsions, surfactants, polymer solutions, rheology, interfacial science, thermophysical fluids, coatings, inks, lubricants, or related fields
- Familiarity with rheology, phase stability, dispersion, aggregation, sedimentation, wetting, surface tension, foaming, thermal conductivity, heat capacity, or related fluid performance 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 complex fluid systems
More about this role
Lila Sciences is seeking a Computational Scientist I/II, Soft Matter Formulations - Complex Fluids to develop models, tools, and workflows that accelerate discovery across liquid and flowable soft material systems. This role focuses on complex fluids, including colloidal suspensions, emulsions, surfactant systems, polymer solutions, coolants and heat-transfer fluids, coatings, inks, and lubricants.
You will bring domain expertise in soft matter, complex fluids, colloids, rheology, interfacial science, formulation science, or a closely related area, and apply machine learning methods to connect composition, microstructure, processing conditions, and bulk fluid properties. The work spans rheology and flow behavior, phase stability, dispersion and aggregation, sedimentation, shelf-life, interfacial and wetting behavior, surface tension, foaming, and thermophysical performance.
This is a hands-on scientific ML role for someone who can bridge domain context and computational execution. You will develop structure-property models linking composition to microstructure and bulk fluid behavior, build active learning workflows over continuous compositional spaces, and incorporate mesoscale...
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