We are a global healthcare leader headquartered in Indianapolis, Indiana. Our 39,000 employees work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. Weu2019re looking for people who are determined to make life better for people around the globe. Competency Summary We are seeking an AI Engineer Intern to design, build, and deploy AI/ML models and intelligent systems that accelerate small molecule drug discovery u2014 from hit identification and lead optimization through to candidate selection. You will work alongside medicinal chemists, computational chemists, and DMPK scientists to develop and productionize deep learning, generative AI, and LLM-powered solutions that extract insights from compound activity, structure, and ADMET data. This role sits at the heart of our computational chemistry ecosystem, applying AI to connect experimental data, predictive models, and public chemical databases into intelligent workflows that enable faster, smarter molecule design. Key Objectives/Deliverables Design and deploy ML pipelines that ingest compound registration, assay activity, and structure-activity relationship (SAR) data to train and serve predictive models for molecular property prediction and hit prioritization Build AI-powered data integration layers that connect public chemical databases (ChEMBL, PubChem, BindingDB, DrugBank) with internal compound management systems to enrich training datasets for ML models. Develop and fine-tune deep learning models on molecular representations (SMILES, molecular graphs, 3D conformers, fingerprints) for tasks such as ADMET prediction, binding affinity estimation, and de novo molecule generation Build and deploy generative AI and LLM-based applications for molecular design, retrosynthesis planning, and automated SAR analysis, including retrieval-augmented generation (RAG) over chemistry literature and patents Implement model evaluation frameworks, monitoring, and MLOps practices (experiment tracking, model versioning, CI/CD) to ensure reliability and reproducibility of AI systems in production. Partner closely with medicinal chemists, computational chemists, and data scientists to translate discovery questions into AI-driven solutions and well-defined model requirements. Minimum Position Qualificationsu00A0 Bacheloru2019s or Masteru2019s degree in Computer Science, Artificial Intelligence, Computational Chemistry, Cheminformatics, or a related quantitative field. Ph.D. preferred. Understanding of small molecule drug discovery workflows u2014 HTS, lead optimization, SAR, selectivity profiling, and ADMET characterization u2014 and how AI/ML can be applied at each stage Working understanding of chemical representations (SMILES, InChI, MOL/SDF), molecular descriptors, and molecular graph structures, with the ability to featurize them for ML model training. Proficiency in Python, including ML frameworks (PyTorch, TensorFlow) and cheminformatics libraries (RDKit, NumPy, pandas, scikit-learn). Experience with Hugging Face or molecular generative models is a plus. Experience with Git/GitHub workflows, CI/CD fundamentals, and exposure to MLOps tooling (MLflow, Weights & Biases, Docker) for experiment tracking and model deployment. Strong proficiency in Python for data processing and model development familiarity with SQL and cloud-based AI platforms (AWS SageMaker, Databricks, or Azure ML).
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Lillyu00A0does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.
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