Applied AI Engineer, Clinical Informatics

Eli Lilly and Company

. Develop and deploy agentic AI applications enabling natural-language interaction with clinical data .

Posted 10/5/2026full-timeBoston • Massachusetts • United StatesMid-LevelSenior💰 $166,500 - $266,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying AI applications for clinical data analysis, utilizing advanced statistical modeling, machine learning, and natural language processing techniques. Proficient in managing clinical trial datasets and ensuring compliance with relevant regulations such as HIPAA and GDPR.

Highest-signal resume keywords

Expert Proficiency In PythonExpert Proficiency In RStrong Command Of SQLExperience With SDTM/ADaMDemonstrated Use Of AI Tools In Production Environments

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical ModellingMachine LearningNatural Language ProcessingSurvival AnalysisCausal InferenceDeep LearningMulti-Omic Data AnalysisKnowledge Graph ConstructionGraph MLOntology-Driven Reasoning

Tools & Technologies

DNAnexusAWSGCPAzureHPC Clusters

Industry Keywords

Biomedical InformaticsComputational BiologyBioinformaticsStatistical GeneticsEpidemiologyClinical Trial DataBiobank DataHIPAA ComplianceGDPR ComplianceIRB Requirements

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonSQL

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 RAG pipelines anchored in biomedical ontologies, clinical trial registries, and curated pathway databases
Deploy unsupervised and self-supervised learning 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
Harmonize heterogeneous trial and biobank datasets to common data representations
Evaluate and monitor model performance, safety, and reliability in production environments
Manage vendors, contractors, and partner relationships across Lilly
Build pipelines for locked clinical trial databases using SDTM and ADaM for secondary and exploratory research
Identify trial subgroup effects, treatment heterogeneity, and responder/non-responder signatures from completed trial data
Mine adverse event narratives, clinical notes, and investigator comments using NLP to surface latent safety signals
Reconstruct patient-level longitudinal trajectories to model disease progression, drug response kinetics, and time-to-event outcomes
Architect meta-analytic and cross-trial integrative workflows across completed studies
Connect to large-scale biobank cohorts such as UK Biobank and All of Us for external validation and enrichment
Establish reproducible research data management practices, including data versioning, containerized compute environments, and audit-ready analysis logs
Ensure research activities comply with HIPAA, GDPR, and relevant IRB and ethics committee requirements

Requirements

What you’ll need

M.S. 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, with 6+ years of research experience working with clinical trial datasets (SDTM/ADaM), biobank data, or large-scale population health data
Or Ph.D. 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, with 3+ years of research experience working with clinical trial datasets (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 modelling and ML
Strong command of SQL
Experience with cloud-based research computing environments, ideally DNAnexus, AWS, GCP, Azure, or HPC clusters
Familiarity with advanced generative AI methods such as LLM fine-tuning and building/training foundation models from scratch
Experience with high-performance computing environments
Deep knowledge of CDISC standards (SDTM, ADaM)
Demonstrated 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
Must follow HIPAA, GDPR, and relevant IRB and ethics committee requirements

Benefits

Comp & perks

Company bonus depending, in part, on company and individual performance
Company-sponsored 401(k)
Pension
Vacation benefits
Medical benefits
Dental benefits
Vision benefits
Prescription drug benefits
Flexible benefits, including healthcare and/or dependent day care flexible spending accounts
Life insurance and death benefits
Time off and leave of absence benefits
Well-being benefits, including employee assistance program, fitness benefits, and employee clubs and activities
Employee resource groups (ERGs)