Scientist I/ii, Drug Delivery Chemistry

Lila Sciences Lila Sciences · AI Frontier · One Charles Park, Cambridge, MA · Autonomous Science Platform

Scientist role focused on designing and developing a high throughput chemical synthesis platform for drug delivery applications, optimizing novel molecules, and building structure-activity relationships. The role involves collaboration with chemistry, engineering, machine learning, and data science teams to enable closed-loop optimization of organic compounds.

What you'd actually do

  1. Design, synthesize, and optimize novel lipids, small molecules, and other combinatorial chemistry compound libraries for drug delivery applications
  2. Design and deploy high-throughput synthesis strategies, build automated assay platforms, and develop innovative approaches to interrogate large chemical spaces
  3. Partner closely with chemistry, computational, and automation teams to establish structure-activity relationships for compound libraries to guide iterative design strategies
  4. Implement data-driven approaches to analyze screening outputs and guide iterative molecule design.
  5. Work with and manage projects with CDMOs for research-grade synthesis, with considerations towards scale-up of lead compounds

Skills

Required

  • Ph.D. in Chemistry, Chemical Engineering, or a related field.
  • ≥ 1 years of experience with automation, instrumentation, and integrating real-time analytics into experimental workflows.
  • Strong background in organic synthesis, with experience designing and synthesizing novel organic compounds.
  • Excellent problem-solving, communication, and collaboration skills.

Nice to have

  • Familiarity with novel lipid design for drug delivery applications
  • Prior knowledge and implementation of biodegradability and tolerability design principles, with working knowledge of biological implications of compound moieties
  • Prior experience working in an autonomous or self-driving chemical lab environment driving organic chemistry transformations
  • Hands-on experience with AI-driven experimental design or Bayesian optimization
  • Background in scaling workflows from proof-of-concept to routine, high-throughput operation

What the JD emphasized

  • high throughput chemical synthesis platform
  • medicinal chemistry
  • drug delivery applications
  • structure-activity relationships
  • closed-loop optimization of organic compounds
  • AI-driven experimental design

Other signals

  • high throughput chemical synthesis platform
  • medicinal chemistry
  • drug delivery applications
  • structure-activity relationships
  • closed-loop optimization of organic compounds