Co-Op, Reaction Prediction & Benchmarking
Lila Sciences · Cambridge, MA USA; San Francisco, CA USA · Alewife, Cambridge, MA · One Charles Park, Cambridge, MA · San Francisco, CA
Bio & Science AIonsiteintern
At a glance
Highlights
- Develop and evaluate ML methods for small molecule design and synthesis.
- Build benchmarks to assess reaction prediction and plausibility models.
- Deliver reproducible workflows and recommendations guiding future model development.
Ideal candidate
Chemistry-minded graduate student curious about AI-driven drug discovery who enjoys hands-on research, reproducible experimentation, and turning chemical data into insights for molecular design.
Requirements
- Pursuing a graduate degree (MSc, PhD) in Chemistry, Computer Science, Physics or similar
- Python for data processing, model training, and evaluation
- Familiarity with cheminformatics libraries like RDKit
- Basic machine learning including supervised learning and evaluation metrics
- Familiarity with chemistry concepts through coursework or research experience
Lab profile
About Lila Sciences
Building an autonomous science platform where AI, automation, and biology co-evolve for medicine and materials.
What they build
- Autonomous Science Platform
- AI-designed biologics validated in wet-lab
- Electro-optic materials and photonic devices
Known for
- Large-scale automated data generation
- High-throughput protein purification and analytics
- Protein design and BioML modeling
- Robotic orchestration and lab simulation
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