Computational Scientist I/II, Soft Matter Formulations - Solids and Melts

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.

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.