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

Lila Sciences; Cambridge (MA); On-site; USD 118,800 - 187,000; Full time

Benefits offered by this job

Equity

Medical, dental, vision benefits

Educational assistance program

Commuter benefits

Job summary

Lila Sciences is seeking a Computational Scientist I/II to develop models and workflows for solids and soft matter formulations. You will bridge domain science in polymers and rheology with computational execution, building structure–property models using experimental and simulation data.

Lead active learning strategies and create interpretable tools for scientists, collaborating with experimental teams to meet performance targets across processing windows and material types.

Qualifications

• PhD in chemical engineering, materials science, physics, applied mathematics, computational science, or a master’s with relevant experience.
• Experience applying ML to polymer, soft matter, or formulation problems.
• Strong Python skills and ML framework experience.
• Ability to connect formulation history to material properties and performance.

Responsibilities

• Develop machine learning models for polymers, elastomers, gels, hot-melts, adhesives, composites, powders, films, semi-solids, and solids.
• Define targets for mechanical performance, thermal transitions and processing windows.
• Build representations linking formulation variables, processing history, morphology, crystallinity, and cure kinetics.
• Create structure–property models for solid and viscoelastic materials using diverse datasets.
• Incorporate molecular descriptors and simulation outputs to improve predictions.
• Design active learning workflows aligned with throughput and lab constraints.
• Develop tools to help scientists interpret data and guide decisions.
• Collaborate with experimental teams to align models with measurement workflows.

Skills

Python

Machine learning

Materials science

Soft matter physics

Rheology

Data modeling

Active learning

Scientific ML

Education

PhD in Chemical Engineering

PhD in Materials Science

PhD in Physics

PhD in Applied Mathematics

PhD in Computational Science

Master’s degree with relevant experience

Tools

Simulation software

Python libraries

Job description

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‑solid, 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.

Compensation

We offer competitive base compensation with bonus potential and generous early‑stage equity. Your final offer will reflect your background, expertise, and expected impact.

U.S. Benefits. Full‑time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer‑paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.

• Medical, dental, and vision coverage
• Employer‑paid life and disability insurance
• Flexible time off with generous company wide holidays
• Paid parental leave
• An educational assistance program
• Commuter benefits, including bike share memberships for office based employees
• A company subsidized lunch program

International Benefits. Full‑time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.

Expected Base Salary Range: $118,800 USD - $187,000 USD

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.

We’re All In

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