Applied AI Engineer, Clinical Informatics

Eli Lilly and Company

Applied AI Engineer building clinical-data and machine-learning systems for Lilly’s life-changing medicines. Extracting patient phenotypes from trials, biobanks, omics, and electronic health records.

Posted 8/4/2026full-timeBoston • Massachusetts • 🇺🇸 United StatesMid-LevelSenior💰 $181,500 - $283,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing AI applications for clinical data analysis, utilizing advanced statistical modeling and machine learning techniques. Proficient in managing clinical trial datasets and ensuring compliance with regulatory standards.

Highest-signal resume keywords

Expert Proficiency In PythonStrong SQL SkillsExperience With Clinical Trial DatasetsKnowledge Of CDISC StandardsFamiliarity With NLP And Deep Learning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical ModelingMachine LearningSurvival AnalysisCausal InferenceGenerative AI MethodsFederated LearningKnowledge GraphsGraph MLMulti-Omic Data AnalysisOntology-Driven Biomedical Reasoning

Tools & Technologies

Cloud-Based Research ComputingHPC ClustersContainerized ComputeVersioning ToolsAudit-Ready Logs

Industry Keywords

Biomedical InformaticsComputational BiologyBioinformaticsClinical Trial RegistriesBiobank DataHIPAA ComplianceGDPR ComplianceIRB RequirementsPharmacogenomicsDrug-Response Modeling

Tech Stack

Tools & technologies

CloudPythonSQL

About the role

Key responsibilities & impact

Develop and deploy agentic AI applications enabling natural-language interaction with clinical data
Ground AI outputs in validated biological knowledge using biomedical ontologies, clinical trial registries, and pathway databases
Apply unsupervised, self-supervised, survival modeling, and dynamic treatment-regime methods to trial and biobank data
Build AI tooling to harmonize heterogeneous datasets into common data representations
Evaluate and monitor model performance, safety, and reliability in production
Manage vendors, contractors, and partner relationships across Lilly
Build pipelines for locked clinical trial databases using SDTM and ADaM
Identify trial subgroup effects, treatment heterogeneity, and responder/non-responder signatures
Mine adverse-event narratives, clinical notes, and investigator comments using NLP
Reconstruct longitudinal patient trajectories to model disease progression, treatment response, and time-to-event outcomes
Architect meta-analytic and cross-trial integrative workflows
Connect to large-scale biobank cohorts for external validation and enrichment
Establish reproducible research data-management practices, including versioning, containerized compute, and audit-ready logs
Ensure research activities comply with HIPAA, GDPR, IRB, and ethics requirements

Requirements

What you’ll need

M.S. in Biomedical Informatics, Computational Biology, Bioinformatics, Statistical Genetics, Epidemiology, Computer Science, or related quantitative field with 6+ years of research experience; or Ph.D. in one of these fields with 3+ years of research experience
MD/PhD with equivalent depth in translational data science accepted
Research experience with clinical trial datasets, including SDTM/ADaM, biobank data, or large-scale population health data
Demonstrated use of AI tools in production environments for clinical data analysis
Expert proficiency in Python and/or R for statistical modeling and machine learning
Strong SQL skills
Experience with cloud-based research computing environments or HPC clusters
Familiarity with generative AI methods, LLM fine-tuning, and foundation-model training
Deep knowledge of CDISC standards and secondary clinical-trial research
Experience with survival analysis, causal inference, NLP, and deep learning
Understanding of OMOP CDM, HL7 FHIR Genomics, and biomedical ontologies
Research experience with major public and restricted-access biobanks such as UK Biobank and All of Us
Experience with federated learning, differential privacy, or secure computation frameworks
Peer-reviewed publication record in clinical AI, translational informatics, genomics, or related fields
Familiarity with the target-trial framework
Knowledge of pharmacogenomics, drug-response modeling, or PK/PD analysis
Experience with knowledge graphs, graph ML, or ontology-driven biomedical reasoning
Hands-on multi-omic data-analysis experience

Benefits

Comp & perks

Company bonus eligibility based partly on company and individual performance
Company-sponsored 401(k)
Pension
Vacation benefits
Medical benefits
Dental benefits
Vision benefits
Prescription drug benefits
Healthcare and/or dependent day care flexible spending accounts
Life insurance and death benefits
Time off and leave of absence benefits
Well-being benefits
Employee assistance program
Fitness benefits
Employee clubs and activities
Employee resource groups (ERGs)