Lilly is seeking to accelerate molecule discovery by developing next-generation AI models and automated pipelines for reaction prediction and condition optimization in small-molecule drug discovery.
Requirements
- Strong background in organic chemistry (particularly in synthesis or methods development), and/or mechanism and reaction modeling.
- Experience with reaction informatics and retrosynthesis algorithms.
- Proficiency in Python and one or more cheminformatics toolkits (e.g., RDKit, CGRtools).
- Experience with machine learning and deep learning frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
- Hands-on experience with HTE platforms or automated chemistry systems.
- Familiarity with eLN systems and AI-to-lab integration.
- Publication record in cheminformatics, reaction prediction, or automated synthesis.
Responsibilities
- Refine deep learning models (e.g., graph-based transformers) for reaction product and condition prediction.
- Curate and integrate reaction datasets from internal sources to train and benchmark models.
- Evaluate external models (e.g., RPBP, NERF) for relevance and applicability across reaction classes.
- Develop tools and APIs to integrate predictive models with high-throughput experimentation (HTE) platforms.
- Enable data-driven reaction condition optimization and parallel library synthesis through automation-friendly workflows.
- Collaborate with the Agentic AI systems to prototype eLN-to-lab automation agents.
- Apply cheminformatics techniques (reaction encoding, fingerprinting, condition clustering) to support model interpretation and experimental planning.
Other
- Ph.D. in organic chemistry, cheminformatics, computational chemistry, or related field (degree must be awarded by start date).
- Work cross-functionally with medicinal chemistry, automation, and AI/ML teams.
- Present findings through internal presentations, external publications, and conferences.
- Ability to work across interdisciplinary teams and contribute to tool deployment or platform development.
- 2-year fixed duration position with potential to extend to a maximum of 4 years.
