Scientist I/II, Drug Delivery Chemistry

Lila Sciences
Cambridge, MA USAPosted 28 April 2026

Job Description

Your Impact at LILA We are seeking a Scientist (II) to join our Physical Sciences team. This role will focus on assisting in the design and development of a high throughput chemical synthesis platform, with emphasis on medicinal chemistry and drug delivery applications. The ideal candidate will have experience synthesizing, analyzing, and optimizing novel molecules to build out structure-activity relationships for a library of compounds. This position will work closely with others across the chemistry, formulations, engineering, machine learning, and data science teams to help establish next-generation tools for therapeutic development and drug delivery. Your work will bridge the physical sciences and bioanalytical domains, enabling high-throughput experimentation for closed-loop optimization of organic compounds. What You'll Be Building Design, synthesize, and optimize novel lipids, small molecules, and other combinatorial chemistry compound libraries for drug delivery applications Design and deploy high-throughput synthesis strategies, build automated assay platforms, and develop innovative approaches to interrogate large chemical spaces Partner closely with chemistry, computational, and automation teams to establish structure-activity relationships for compound libraries to guide iterative design strategies Implement data-driven approaches to analyze screening outputs and guide iterative molecule design. Work with and manage projects with CDMOs for research-grade synthesis, with considerations towards scale-up of lead compounds Establish best practices for screening data quality, reproducibility, and assay robustness. Collaborate across disciplines to translate screening hits into validated chemical starting points for therapeutic programs. What You’ll Need to Succeed 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. Bonus Points For 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 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 $170,000 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 tha ... (truncated, view full listing at source)
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