Your Impact at LILA

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 constraints, and design active learning workflows tied to the throughput of the physical formulation workcell.

What You'll Be Building

• Develop machine learning models for polymers, elastomers, gels, hot-melts, adhesives, composites, powders, films, semi-solids, and crystalline or amorphous solids.
• Define modeling targets for mechanical performance, thermal transitions and processing windows, and processing-sensitive material responses.
• Build representations that connect formulation variables, processing history, morphology, crystallinity, cross-link density, cure kinetics, and end-use properties.
• Develop structure-property models for solid and viscoelastic materials using experimental, simulation, rheological, thermal, mechanical, and formulation datasets.
• Incorporate molecular descriptors, polymer descriptors, simulation outputs, and mechanistic constraints where they improve prediction or interpretation.
• Build active learning workflows that prioritize formulation experiments in line with physical formulation workcell throughput and lab constraints.
• Create tools that help scientists interpret material data and prioritize formulation, processing, or composition decisions.
• Partner with experimental teams to align models with measurement workflows, material performance requirements, and practical formulation development needs.
• Communicate model behavior, uncertainty, and recommendations to scientific, engineering, and cross-functional collaborators.

What You'll Need to Succeed

• 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.
• Strong communication skills with experimental, computational, and cross-functional collaborators.
• PhD in chemical engineering, materials science, physics, applied mathematics, computational science, or a related field, or a master’s degree with equivalent relevant experience.

Bonus Points For

• Experience working with experimental data from polymers, elastomers, gels, hot-melts, adhesives, composites, powders, films, semi-solids, or solid formulations.
• Experience modeling structure-property relationships for solid, semi-solid, or viscoelastic materials.
• Familiarity with cure- or processing-aware representations for formulation, thermal, mechanical, or rheological datasets.
• Experience incorporating molecular or polymer descriptors, simulation constraints, theoretical models, or mechanistic priors into ML workflows.
• Background in active learning systems that close the loop between models and high-throughput physical experimentation.
• Hands-on experimental or computational experience in polymer, adhesive, gel, composite, or soft material formulation domains.