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Lila Sciences

AI-powered scientific discovery for medicines

Senior Scientist - Analytical Chemistry

Translational Research & Bench BiologyConfirmed live in the last 24 hours
Full-timeCambridge, MA, USAIn-person

About the Job

Requirements

  • A PhD in Analytical Chemistry, Organic Chemistry, Pharmaceutical Sciences, Chemical Biology, or a related scientific discipline, plus 5–8+ years of relevant industry or high-performance research experience.
  • Deep expertise in analytical chemistry and molecular characterization in support of chemistry-driven discovery efforts.
  • Strong hands-on experience with key analytical techniques for small molecule analysis.
  • Demonstrated experience developing analytical methods for compound characterization, reaction monitoring, impurity analysis, and purity assessment.
  • Experience supporting Discovery Chemistry, medicinal chemistry, synthetic chemistry, or other molecule-focused research and development environments.
  • Strong understanding of how analytical data informs compound progression, screening decisions, reaction optimization, and molecular design.
  • Experience working with or supporting high-throughput workflows, including rapid analytical turnaround in fast-paced research settings.
  • Ability to solve complex analytical problems independently and work effectively across interdisciplinary teams spanning chemistry, screening, automation, and computational sciences.
  • Strong documentation, communication, and data interpretation skills, with a commitment to rigor, reproducibility, and scientific quality.

Responsibilities

  • Develop, optimize, and implement analytical and multimodal characterization workflows to support Discovery Chemistry programs and molecular discovery campaigns.
  • Support the design, synthesis, screening, and optimization of small molecules by delivering rapid, reliable, and information-rich analytical data.
  • Lead analytical method development for compound identification, purity assessment, impurity profiling, reaction monitoring, and molecular characterization using techniques such as liquid chromatography, mass spectroscopy, NMR, and complementary orthogonal methods.
  • Build characterization strategies that incorporate multimodal measurements to provide insight into molecular identity, reaction outcomes, physicochemical properties, and structure–function relationships.
  • Partner with Discovery Chemistry and screening teams to support high-throughput screening and experimentation workflows, including rapid sample analysis, reaction triage, and compound profiling.
  • Collaborate with automation and platform teams to integrate analytical instrumentation and data pipelines into robotics-enabled and scalable discovery workflows.
  • Work with AI and computational teams to ensure analytical datasets are structured, interoperable, and useful for model development, closed-loop learning, and iterative molecule design.
  • Establish robust workflows for reaction monitoring, in-process analytics, final compound quality assessment, and characterization of complex mixtures or intermediates.
  • Troubleshoot analytical and characterization challenges related to sample preparation, instrumentation, method robustness, data interpretation, and workflow integration.
  • Contribute to platform-level capabilities for data-rich molecular discovery, including scalable characterization pipelines, method standardization, and best practices for analytical rigor.

Desired Qualifications

  • Experience with multimodal characterization approaches that combine orthogonal measurements to generate deeper insight into molecular systems and discovery workflows.
  • Experience supporting high-throughput screening, reaction screening, or automated discovery platforms.
  • Familiarity with analytical workflows integrated with robotics, liquid handlers, autosamplers, or other automation-enabled laboratory systems.
  • Experience working in AI-enabled or highly data-driven research and development environments where experimental data is used to drive predictive modeling or closed-loop optimization.
  • Knowledge of characterization approaches extending beyond traditional compound quality control, including molecular profiling, reaction analytics, and property-focused measurements.
  • Familiarity with data systems such as electronic laboratory notebooks (ELNs), laboratory information management systems (LIMS), and software tools used to manage and analyze large analytical datasets.

About the Company

Lila Sciences

AI-powered scientific discovery for medicines

Lila.ai uses artificial intelligence to speed up scientific discovery in medicines and advanced materials. The system ingests large sets of scientific data, experimental results, and literature to identify patterns, generate hypotheses, and guide research programs. It differentiates itself by tackling deep scientific problems at scale and combining AI with domain expertise, rather than offering generic tools. The goal is to help humanity by delivering new medicines and materials that solve major biological and materials challenges through data-driven discovery.

Company size
501-1,000
Company stage
Series A
Total funding
$550M
Headquarters
Cambridge, MA
Founded
2024

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