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Platform Scientist, Soft Materials

Lila Sciences
Cambridge, MA USAPosted 9 May 2026

Job Description

Your Impact at LILA Lila Sciences is seeking Platform Scientists, Soft Materials Focus to ideate, prototype, and transfer new experimental concepts for AI Science Factories targeting complex and soft material systems. In this role, you will work at the forefront of scientific ideation—partnering with a broad range of chemistry, materials, and AI teams to define new approaches to formulation, structure–property relationships, and performance evaluation. You will translate these concepts into scalable experimental workflows, custom apparatus, and software-enabled systems for autonomous research. What You'll Be Building Collaborate with diverse scientific teams to ideate new experimental strategies for soft and complex materials, including formulations, colloids, polymers, gels, and biological or hybrid systems Lead conceptual development of characterization and performance workflows leveraging techniques such as SAXS, SANS, rheology, and formulation performance assays Design, prototype, and validate custom apparatus, experimental methods, and software tools to realize these concepts Develop and refine workflows that connect structure, dynamics, and performance across multiple length and time scales Transfer successful prototypes into production-ready systems within Lila’s AI Science Factory platform, enabling autonomous experimentation What You’ll Need to Succeed MS or PhD in Materials Science, Chemistry, Chemical Engineering, Physics, or a related field, with a focus on soft or complex materials Demonstrated experience collaborating across scientific disciplines to generate and refine experimental concepts Hands-on experience with one or more of the following: SAXS, SANS, rheology, formulation performance or functional assays Experience designing and implementing custom experimental workflows and/or scientific software Strong programming skills (e.g., Python, C++, or similar) applied to data analysis, automation, or experiment control Excellent communication skills and comfort operating in a fast-paced, exploratory RD environment Bonus Points For Experience with lab automation, high-throughput experimentation, or instrument control software Background in formulation science (e.g., consumer products, coatings, energy materials, bio-soft matter) Experience linking scattering, rheological, and performance data to predictive or ML-driven models Familiarity with prototyping tools such as 3D printing, machining, microfluidics, or custom sample environments Industry or national lab experience working on soft materials or complex fluids 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. 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 $108,000 $150,000 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 medi ... (truncated, view full listing at source)
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