Applied AI Engineer, Clinical Informatics
Eli Lilly and Company
8 days ago
Remote friendly (Indianapolis, IN)
United States
IT
Key Responsibilities
- Develop and deploy agentic AI applications enabling natural language interaction with clinical data
- Ground AI outputs in validated biological knowledge (e.g., RAG pipelines using HPO, Gene Ontology, MeSH, DrugBank; clinical trial registries; curated pathway databases)
- Deploy unsupervised/self-supervised learning (e.g., clustering, representation learning, contrastive learning) to discover latent patient archetypes and molecular disease subtypes
- Deploy survival models and dynamic treatment regime estimators using combined clinical and omics features
- Build AI tooling to harmonize heterogeneous trial/biobank datasets to common representations
- Evaluate/monitor model performance, safety, and reliability in production
- Manage vendors/contractors and partner relationships
Post-Trial Data Research & Analysis
- Build pipelines for locked clinical trial databases (SDTM, ADaM) for secondary/exploratory research beyond primary endpoints
- Deploy ML workflows to identify trial subgroup effects, treatment heterogeneity, and responder/non-responder signatures
- Use NLP to mine adverse event narratives, clinical notes, and investigator comments for latent safety signals
- Reconstruct patient-level longitudinal trajectories for disease progression, drug response kinetics, and time-to-event outcomes
- Architect cross-trial integrative meta-analytic analyses
- Connect trial findings to large-scale biobanks (e.g., UK Biobank, All of Us) for external validation/enrichment
Research Rigor, Reproducibility & Governance
- Establish reproducible research practices (data versioning, containerized compute, audit-ready logs)
- Ensure research activities follow HIPAA, GDPR, and relevant IRB/ethics requirements
Basic Qualifications
- M.S. (or MD/PhD equivalent) with 6+ years research experience with clinical trial (SDTM/ADaM) and/or biobank/population health data; OR Ph.D. (or MD/PhD equivalent) with 3+ years research experience
Additional Skills & Preferences
- AI tools in production; Python/R, strong SQL; cloud/HPC experience (DNAnexus, AWS, GCP, Azure, HPC)
- Generative AI (LLM fine-tuning or foundation model building); CDISC (SDTM/ADaM)
- ML methods: survival analysis, causal inference, NLP, deep learning
- OMOP CDM, HL7 FHIR Genomics, biomedical ontologies
- Biobank experience; federated learning/differential privacy/secure computation; publications
- Knowledge of target trial framework; pharmacogenomics/PK/PD; knowledge graphs/graph ML; multi-omic analysis
Application Instructions
- Apply today!
- Develop and deploy agentic AI applications enabling natural language interaction with clinical data
- Ground AI outputs in validated biological knowledge (e.g., RAG pipelines using HPO, Gene Ontology, MeSH, DrugBank; clinical trial registries; curated pathway databases)
- Deploy unsupervised/self-supervised learning (e.g., clustering, representation learning, contrastive learning) to discover latent patient archetypes and molecular disease subtypes
- Deploy survival models and dynamic treatment regime estimators using combined clinical and omics features
- Build AI tooling to harmonize heterogeneous trial/biobank datasets to common representations
- Evaluate/monitor model performance, safety, and reliability in production
- Manage vendors/contractors and partner relationships
Post-Trial Data Research & Analysis
- Build pipelines for locked clinical trial databases (SDTM, ADaM) for secondary/exploratory research beyond primary endpoints
- Deploy ML workflows to identify trial subgroup effects, treatment heterogeneity, and responder/non-responder signatures
- Use NLP to mine adverse event narratives, clinical notes, and investigator comments for latent safety signals
- Reconstruct patient-level longitudinal trajectories for disease progression, drug response kinetics, and time-to-event outcomes
- Architect cross-trial integrative meta-analytic analyses
- Connect trial findings to large-scale biobanks (e.g., UK Biobank, All of Us) for external validation/enrichment
Research Rigor, Reproducibility & Governance
- Establish reproducible research practices (data versioning, containerized compute, audit-ready logs)
- Ensure research activities follow HIPAA, GDPR, and relevant IRB/ethics requirements
Basic Qualifications
- M.S. (or MD/PhD equivalent) with 6+ years research experience with clinical trial (SDTM/ADaM) and/or biobank/population health data; OR Ph.D. (or MD/PhD equivalent) with 3+ years research experience
Additional Skills & Preferences
- AI tools in production; Python/R, strong SQL; cloud/HPC experience (DNAnexus, AWS, GCP, Azure, HPC)
- Generative AI (LLM fine-tuning or foundation model building); CDISC (SDTM/ADaM)
- ML methods: survival analysis, causal inference, NLP, deep learning
- OMOP CDM, HL7 FHIR Genomics, biomedical ontologies
- Biobank experience; federated learning/differential privacy/secure computation; publications
- Knowledge of target trial framework; pharmacogenomics/PK/PD; knowledge graphs/graph ML; multi-omic analysis
Application Instructions
- Apply today!