Co-Op, Reaction Prediction & Benchmarking
About the company
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 that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
Your tasks
Your Impact at LILA Develop and evaluate machine learning methods supporting small molecule design and synthesis. Working with public and internal chemistry datasets, the co-op will pursue a focused research project in either reaction prediction and benchmarking or molecular property prediction for medicinal chemistry optimization.
Potential projects include building benchmarks to assess reaction prediction models and reaction plausibility, or developing and fine-tuning molecular property predictors using project-specific data. The primary project will be selected based on the candidate’s interests, experience, and team priorities. Expected outputs include reproducible data and evaluation workflows, benchmark results, and research recommendations to inform future model development and integration.
What You'll Be Building
- Plan and execute a Co-Op project in conjunction with the Molecular Intelligence team
Requirements
What You'll Need to Succeed
- Pursuing a graduate degree (MSc, PhD) in Chemistry, Computer Science, Physics, or similar
Bonus Points For
- Python (data processing, model training, and evaluation)
- Familiarity with cheminformatics libraries (RDKit or similar)
- Basic machine learning (supervised learning, model evaluation metrics)
- Familiarity with chemistry concepts through coursework or research experience
- Bonus: experience with molecular property prediction, multi-task learning, self-supervised pre-training, or model fine-tuning
- Bonus: familiarity with reaction data formats, reaction SMARTS, or reaction databases (e.g., Pistachio, Reaxys, SciFinder)
- Bonus: background or interest in organic or medicinal chemistry
Other
We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.