Lila Sciences

Lila Sciences

Platform Scientist, Soft Materials

Cambridge, MA USA · Full-time

Sponsorship not specified$108k-$150kDetected 16 days ago
PythonC++Machine LearningData AnalysisA/B TestingRecruitingCommunication

About the role

  • 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.
  • Your final offer will reflect your background, expertise, and expected impact.

Responsibilities

  • Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges.
  • Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
  • 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

Requirements

  • Experience with lab automation, high-throughput experimentation, or instrument control software

Compensation

  • We offer competitive base compensation with bonus potential and generous early-stage equity.
  • 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

Visa & Work Authorization

  • al employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status

This listing is sourced directly from Lila Sciences's careers page and normalized into a canonical job model.