Advisor/ Sr. Advisor - AI for Science (admet Intelligence)

Eli Lilly Eli Lilly · Pharma · Indianapolis, IN +4

Research Advisor/Sr. Advisor role focused on applying and developing innovative AI/ML models for drug discovery, specifically in ADMET Intelligence. The role involves integrating AI/ML with Chemistry and Biology, developing scientific foundation models, and collaborating with experimental scientists. The goal is to advance drug discovery by connecting computational predictions with lab experiments and publishing research findings.

What you'd actually do

  1. Drive drug discovery programs by developing and applying innovative AI/ML models.
  2. Collaborate closely with experimental scientists to connect computational predictions with lab experiments. You will support hypothesis testing and help iterate between modeling and experimental results.
  3. Lead the development of scientific foundation models that integrate molecular design and biological data, gaining experience with approaches that support decision‑making across the drug discovery pipeline.
  4. Advance the field by publishing findings in top-tier venues and representing Lilly at the forefront of the global AI4Science community.
  5. Work with MLOps and Data Engineering partners to help transition research prototypes into scalable tools, learning guidelines for building reusable and reliable AI/ML solutions for discovery teams.

Skills

Required

  • PhD in Computer Science, Machine Learning, Statistics, Computational Chemistry, Computational Biology, Physics, or a Scientific field.
  • Experience in modern AI/ML frameworks and architectures relevant to molecular data (e.g. GNN, Transformers etc.).

Nice to have

  • Demonstrated ability to translate chemistry and biology problems into machine learning formulations.
  • Experience collaborating with lab scientists to develop AI/ML solutions that drive the next cycle of experimentation and model refinement

What the JD emphasized

  • Innovation in science evidenced by first-author publications in high-impact journals or top-tier ML conferences.

Other signals

  • AI/ML models for drug discovery
  • scientific foundation models
  • connect computational predictions with lab experiments
  • publish findings in top-tier venues