Applied AI Engineer, Clinical Informatics

Lilly • Indianapolis, IN

5d • $166,500 - $266,200

About The Position

Lilly is seeking a highly specialized Applied AI Engineer Clinical Informatician to conduct hands-on research at the intersection of completed clinical trial datasets and biobank-linked population data. This is an individual-contributor research role, not operational trial management. The role builds systems and tools to extract, define, and contextualize patient phenotypes from locked trial databases, real-world data, and biobank cohorts, generating translational insight to inform future clinical research.

The engineer will work with already-collected locked trial databases, archived omics profiles, longitudinal electronic health records, and population-scale biobank cohorts. The position develops AI and machine-learning systems to make these datasets manageable and ready for detailed analysis, while collaborating with partners across Lilly and managing relevant vendor and contractor relationships.

The work includes developing agentic AI applications and biomedical knowledge-grounded retrieval systems; discovering patient archetypes and molecular disease subtypes; modeling survival and treatment effects; harmonizing heterogeneous datasets; conducting secondary and exploratory research on completed trials; and integrating trial findings with biobank cohorts. The role also establishes reproducible research data practices and ensures research complies with HIPAA, GDPR, and applicable IRB and ethics requirements.

Requirements

• Master’s degree in Biomedical Informatics, Computational Biology, Bioinformatics, Statistical Genetics, Epidemiology, Computer Science, or a closely related quantitative field, or an MD/PhD with equivalent depth in translational data science, plus at least 6 years of research experience with clinical trial datasets (SDTM/ADaM), biobank data, or large-scale population health data in an academic, pharmaceutical, or research institute setting; alternatively, a PhD in one of these fields or an MD/PhD with equivalent depth in translational data science, plus at least 3 years of such research experience.
• Expert proficiency in Python and/or R for statistical modeling and machine learning.
• Strong command of SQL and experience with cloud-based research computing environments.

Nice To Haves

• Demonstrated use of AI tools in production environments for clinical data analysis.
• Experience with cloud research computing environments such as DNAnexus, AWS, GCP, Azure, or HPC clusters.
• Familiarity with advanced generative AI methods, including fine-tuning LLMs, building and training foundation models from scratch, and high-performance computing environments.
• Deep knowledge of CDISC standards (SDTM, ADaM) and experience analyzing clinical trial databases for secondary research.
• Experience applying survival analysis, causal inference, NLP, and deep learning to clinical or genomic research questions.
• Thorough understanding of OMOP CDM, HL7 FHIR Genomics, and major biomedical ontologies.
• Direct research experience with major public and restricted-access biobank resources such as UK Biobank and All of Us.
• Experience with federated learning, differential privacy, or secure computation frameworks applied to multi-site biomedical research.
• Track record of peer-reviewed publications in clinical AI, translational informatics, genomics, or a related field.
• Familiarity with the target trial framework and its application in biobanks.
• Knowledge of pharmacogenomics, drug response modeling, or PK/PD data analysis from clinical trials.
• Experience with knowledge graph construction, graph ML, or ontology-driven reasoning for biomedical discovery.
• Hands-on experience with multi-omic data analysis.

Responsibilities

• Develop and deploy agentic AI applications that enable natural-language interaction with clinical data.
• Ground AI outputs in validated biological knowledge, including through RAG pipelines anchored in biomedical ontologies, clinical trial registries, and curated pathway databases.
• Apply unsupervised and self-supervised learning methods to discover latent patient archetypes and molecular disease subtypes across trial and biobank data.
• Deploy survival models and dynamic treatment regime estimators using combined clinical and omics features.
• Develop AI tools to harmonize heterogeneous trial and biobank datasets into common data representations.
• Evaluate and monitor model performance, safety, and reliability in production environments.
• Manage vendors and contractors and coordinate partner relationships with relevant Lilly teams.
• Build pipelines for locked clinical trial databases, including SDTM and ADaM, to support secondary and exploratory research beyond primary endpoints.
• Use machine-learning workflows to identify trial subgroup effects, treatment heterogeneity, and responder/non-responder signatures in completed trial data.
• Use NLP to mine adverse-event narratives, clinical notes, and investigator comments for latent safety signals in biobank and clinical datasets.
• Reconstruct patient-level longitudinal trajectories from trial visit data to model disease progression, drug response kinetics, and time-to-event outcomes.
• Architect meta-analytic and cross-trial integrative workflows to identify generalizable biological and clinical patterns across completed studies.
• Connect to large-scale biobank cohorts, such as UK Biobank and All of Us, for external validation and enrichment of trial-derived findings for clinical phenotypes.
• Establish reproducible research data management practices, including data versioning, containerized compute environments, and audit-ready analysis logs.
• Ensure research activities follow HIPAA, GDPR, and relevant IRB and ethics committee requirements.

Benefits

• Company bonus eligibility, depending in part on company and individual performance.
• Company-sponsored 401(k) participation eligibility.
• Pension.
• Vacation benefits.
• Medical, dental, vision, and prescription drug benefits eligibility.
• Flexible benefits, including healthcare and/or dependent day care flexible spending accounts.
• Life insurance and death benefits.
• Certain time-off and leave-of-absence benefits.
• Well-being benefits, including an employee assistance program, fitness benefits, and employee clubs and activities.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior