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Lila Sciences

AI-powered scientific discovery for medicines

Reaction Prediction & Benchmarking Co-op

Applied Machine LearningConfirmed live in the last 24 hours
InternshipNo salary listedIn-person

About the Job

Requirements

  • Pursuing a graduate degree (MSc or PhD) in Chemistry, Computer Science, Physics, or a similar field.

Responsibilities

  • Develop and evaluate machine learning methods supporting small molecule design and synthesis.
  • Work with public and internal chemistry datasets on a focused research project in reaction prediction and benchmarking or molecular property prediction for medicinal chemistry optimization.
  • Build benchmarks to assess reaction prediction models and reaction plausibility, or develop and fine-tune molecular property predictors using project-specific data.
  • Plan and execute a Co-op project in conjunction with the Molecular Intelligence team.
  • Produce reproducible data and evaluation workflows, benchmark results, and research recommendations to inform future model development and integration.

Desired Qualifications

  • Python for data processing, model training, and evaluation.
  • Familiarity with cheminformatics libraries such as RDKit or similar.
  • Basic machine learning knowledge, including supervised learning and model evaluation metrics.
  • Familiarity with chemistry concepts through coursework or research experience.
  • Experience with molecular property prediction, multi-task learning, self-supervised pre-training, or model fine-tuning.
  • Familiarity with reaction data formats, reaction SMARTS, or reaction databases such as Pistachio, Reaxys, or SciFinder.
  • Background or interest in organic or medicinal chemistry.

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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