Director, Discovery Chemistry

Lila Sciences Lila Sciences · AI Frontier · Alewife, Cambridge, MA · Autonomous Science Platform

This role leads the design, synthesis, and optimization of small molecules for a discovery platform, translating AI-generated hypotheses into validated chemical matter. It involves building a chemistry program that integrates with AI, automation, and computational teams, driving synthetic strategy, route design, and molecular property optimization, with a focus on scalable chemistry execution and enabling seamless integration with robotic synthesis.

What you'd actually do

  1. Drive discovery chemistry strategy across discovery programs, with strong emphasis on small molecule design, synthetic innovation, analog generation, and optimization of chemical matter.
  2. Lead the design and execution of synthetic routes for novel small molecules, emphasizing speed, robustness, scalability, and compatibility with automated synthesis platforms.
  3. Develop and implement efficient workflows for molecular design, compound prioritization, analog generation, and iterative design–make–test cycles in collaboration with AI and computational teams.
  4. Partner closely with automation and platform teams to establish robotic and high-throughput chemistry capabilities that expand accessible chemical space and accelerate molecule generation.
  5. Guide the selection of reagents, reaction modalities, and synthetic methodologies to enable rapid exploration of structurally diverse and functionally relevant chemical matter.

Skills

Required

  • PhD in Organic Chemistry, Medicinal Chemistry, Chemical Biology, or a closely related discipline
  • 12–15 years of experience in small molecule discovery and molecular design
  • Demonstrated experience building and leading high-performing chemistry teams
  • Proven track record advancing small molecule programs through molecular design, high-throughput synthesis, optimization, and candidate progression
  • Deep expertise in synthetic organic chemistry, retrosynthesis, route design, and practical execution of complex multistep syntheses
  • Strong experience optimizing molecular properties, including potency, selectivity, physicochemical behavior, and overall developability of small molecule series
  • Demonstrated success designing and delivering compound libraries or analog series to efficiently explore chemical space and address multiparameter optimization challenges
  • Experience working effectively in highly interdisciplinary environments spanning chemistry, biology, automation, and computational sciences
  • Experience collaborating with biology, screening, pharmacology, or related functions to incorporate experimental data into chemistry decision-making
  • Strong understanding of compound quality standards, purification and characterization workflows, and chemistry data integrity
  • Track record of scientific leadership, program delivery, and advancing discovery-stage molecules in an industrial or high-performance research environment

Nice to have

  • Experience working in AI-enabled discovery environments or integrating computational design and machine learning insights into chemistry workflows
  • Experience building or scaling automated, robotics-enabled, or high-throughput chemistry platforms for molecular synthesis and optimization

What the JD emphasized

  • 12–15 years of experience in small molecule discovery and molecular design
  • building and leading high-performing chemistry teams
  • advancing small molecule programs through molecular design, high-throughput synthesis, optimization, and candidate progression
  • synthetic organic chemistry, retrosynthesis, route design, and practical execution of complex multistep syntheses
  • optimizing molecular properties, including potency, selectivity, physicochemical behavior, and overall developability of small molecule series
  • designing and delivering compound libraries or analog series to efficiently explore chemical space and address multiparameter optimization challenges
  • working effectively in highly interdisciplinary environments spanning chemistry, biology, automation, and computational sciences
  • integrating computational design and machine learning insights into chemistry workflows