Applied AI Engineer, Clinical Informatics
USD 181K-283K Senior-level Full Time
Tasks
- Apply unsupervised learning to discover patient archetypes
- Build AI and machine learning systems for clinical data
- Build pipelines for SDTM and ADaM secondary research
- Build survival models and dynamic treatment estimators
- Conduct meta analytic cross trial integrative analyses
- Develop agentic AI applications for natural language interaction
- Ensure HIPAA GDPR and IRB compliance
- Establish reproducible data management practices with versioning and audit logs
- Evaluate and monitor model performance and safety
- Harmonize heterogeneous trial and biobank datasets
- Identify trial subgroup effects and treatment heterogeneity
- Implement RAG pipelines using biomedical ontologies
- Reconstruct patient level longitudinal trajectories
- Use NLP to mine adverse event narratives and clinical notes
- Validate findings using biobank cohorts
Perks/Benefits
- N/A
Skills/Tech-stack
ADaM | Biomedical Ontologies | CDISC | Causal Inference | Clinical data | Clinical data standards | Clustering | Contrastive Learning | Data Analysis | Data Standards | Deep learning | Dynamic Treatment Regimes | Electronic Health Record | Electronic Health Records | Electronic health record analysis | FHIR | FHIR Genomics | Fine Tuning | Foundation Models | Generative AI | Genomic Data Analysis | Genomic data | Graph Machine Learning | HL7 FHIR | HL7 FHIR Genomics | HPC | Health Records | Knowledge graphs | Language Models | Language Processing | Large Language Models | Longitudinal data | Longitudinal data analysis | Machine Learning | Multi-Omics | Natural Language | Natural Language Processing | OMOP | OMOP-CDM | Ontology Engineering | Python | R | RAG | Record Analysis | Representation Learning | SDTM | SQL | Survival analysis
Education
MD | Master of Science | PhD
Roles
AI Engineer | Applied AI Engineer | Clinical Data Scientist | Data Scientist | Engineer | Scientist
Regions
Countries
States
Cities
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