Lead AI Engineer, Biomedical & Vigilance Innovation
United Therapeutics Corporation
August 10, 2026
Remote friendly (Raleigh-Durham-Chapel Hill Area)
United States
IT
Responsibilities:
- Design, build, validate, and maintain ML, NLP, and generative AI solutions for biomedical and pharmacovigilance use cases.
- Develop tools for adverse event intake, case triage, coding assistance, duplicate detection, signal prioritization, and trend analysis.
- Engineer predictive models to identify emerging risks, patient patterns, and operational bottlenecks.
- Translate scientific/business requirements into production-ready AI applications.
- Define and execute a technology strategy for AI in global patient safety operations and signal detection, including agentic automation.
- Build pharmacovigilance AI platforms; integrate structured/unstructured safety, clinical, literature, and RWE data.
- Create staging schemas, data-mining approaches, and scalable ingestion/transform/QC pipelines.
- Apply ontology/terminology harmonization (e.g., MedDRA/WHO Drug) and ensure data lineage/audit readiness.
- Modernize pharmacovigilance/organovigilance with AI-enabled automation/decision support; improve case processing, review, and governance reporting.
- Build surveillance models for novel modalities; develop dashboards for safety trend visualization.
- Ensure GxP, privacy, security, validation, and regulatory alignment; implement model governance (monitoring, XAI, change control) and lifecycle documentation.
Minimum Qualifications:
- BS (8+ yrs) / MS (6+ yrs) / PhD (2+ yrs) in a relevant field.
- 5+ yrs AI engineering/ML/advanced analytics in biopharma/healthcare/regulated industries; 5+ yrs hands-on AI tooling (Python/R/Matlab; TensorFlow/PyTorch; cloud ML).
- Production track record in complex domains; strong cloud-native/software/platform engineering; ability to communicate to non-technical stakeholders.
Preferred Qualifications:
- 5+ yrs NLP/LLMs/knowledge graphs/biomedical text mining; safety systems experience (e.g., Argus/ArisG/Veeva).
- Pharmacovigilance/clinical development/regulatory knowledge; PK/PD modeling; familiarity with GVP/FDA/EMA/ICH and data privacy.
Benefits (if eligible): medical/dental/vision/prescription, wellness resources, 401k/ESPP, paid time off/parental leave, disability, and more.
- Design, build, validate, and maintain ML, NLP, and generative AI solutions for biomedical and pharmacovigilance use cases.
- Develop tools for adverse event intake, case triage, coding assistance, duplicate detection, signal prioritization, and trend analysis.
- Engineer predictive models to identify emerging risks, patient patterns, and operational bottlenecks.
- Translate scientific/business requirements into production-ready AI applications.
- Define and execute a technology strategy for AI in global patient safety operations and signal detection, including agentic automation.
- Build pharmacovigilance AI platforms; integrate structured/unstructured safety, clinical, literature, and RWE data.
- Create staging schemas, data-mining approaches, and scalable ingestion/transform/QC pipelines.
- Apply ontology/terminology harmonization (e.g., MedDRA/WHO Drug) and ensure data lineage/audit readiness.
- Modernize pharmacovigilance/organovigilance with AI-enabled automation/decision support; improve case processing, review, and governance reporting.
- Build surveillance models for novel modalities; develop dashboards for safety trend visualization.
- Ensure GxP, privacy, security, validation, and regulatory alignment; implement model governance (monitoring, XAI, change control) and lifecycle documentation.
Minimum Qualifications:
- BS (8+ yrs) / MS (6+ yrs) / PhD (2+ yrs) in a relevant field.
- 5+ yrs AI engineering/ML/advanced analytics in biopharma/healthcare/regulated industries; 5+ yrs hands-on AI tooling (Python/R/Matlab; TensorFlow/PyTorch; cloud ML).
- Production track record in complex domains; strong cloud-native/software/platform engineering; ability to communicate to non-technical stakeholders.
Preferred Qualifications:
- 5+ yrs NLP/LLMs/knowledge graphs/biomedical text mining; safety systems experience (e.g., Argus/ArisG/Veeva).
- Pharmacovigilance/clinical development/regulatory knowledge; PK/PD modeling; familiarity with GVP/FDA/EMA/ICH and data privacy.
Benefits (if eligible): medical/dental/vision/prescription, wellness resources, 401k/ESPP, paid time off/parental leave, disability, and more.