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Lila Sciences

AI-powered scientific discovery for medicines

Targeted Non-Viral Delivery Co-op

Bioprocess & BiomanufacturingConfirmed live in the last 24 hours
InternshipCambridge, MA, USANo salary listed

About the Job

Requirements

  • Pursuing a Bachelor of Science or Master of Science in chemical engineering, biomedical engineering, pharmaceutical sciences, biology, or a related field.
  • Have hands-on wet-lab experience and be comfortable following detailed standard operating procedures.
  • Have data analysis skills and the ability to interpret results and iterate on experimental design.
  • Provide clear written and verbal communication, including electronic lab notebook documentation.
  • Be available for the full January co-op term onsite at Alewife.

Responsibilities

  • Formulate and characterize targeted lipid nanoparticles on an automated microfluidic workcell.
  • Run high-throughput screens of ionizable lipids and targeting ligands.
  • Execute downstream characterization assays, including dynamic light scattering, RiboGreen, and TNS.
  • Onboard new workflows and experiment types onto the workcell.
  • Test and document changes that increase screening throughput.
  • Record methods, results, and observations in the electronic lab notebook for reuse across teams.
  • Produce targeted lipid nanoparticles to support ongoing experiments.
  • Develop new conjugation and purification methods for targeted lipid nanoparticles and apply those methods on the automated system.

Desired Qualifications

  • Experience with lipid nanoparticle formulation, microfluidics, or other drug delivery systems.
  • Familiarity with automated systems or high-throughput screening platforms.
  • Familiarity with protein characterization methods.
  • Working knowledge of bioanalytical techniques such as fluorescence spectroscopy and electrophoresis.

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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