Lead AI Engineer, Biomedical & Vigilance Innovation Software

Lead AI Engineer, Biomedical & Vigilance Innovation Own and drive the development of production-grade AI platforms for pharmacovigilance and safety signal detection

Location:

North Carolina, United States

United Therapeutics is a biopharma company focusing on rare and cutting‑edge therapies.

We are seeking a Lead AI Engineer with a start‑up mindset to design, develop, and deploy advanced AI solutions that transform how biomedical insights are generated and how safety signals are detected, assessed, and acted upon. The role operates at the intersection of data science, software engineering, and medical safety, applying advanced analytics, machine learning, and automation to create scalable platforms that support proactive risk management and accelerate informed decision‑making across the product lifecycle.

• Design, build, validate, and maintain machine learning, natural language processing, and generative AI solutions for biomedical and pharmacovigilance use cases.
• Develop tools that support 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 complex scientific and business requirements into production‑ready AI applications.
• Own model definition, fine‑tuning, and optimization to ensure fit‑for‑purpose AI solutions for the UT patient safety business.
• Define and execute a bold technology strategy spanning global patient safety, embedding AI, machine learning, and agentic automation across day‑to‑day PV operations, analytics, and signal detection.
• Drive the architecture, development, and delivery of next‑generation platforms for pharmacovigilance AI initiatives.
• Integrate structured and unstructured data from safety databases, clinical systems, literature, real‑world evidence, and external repositories.
• Create staging schemas and mine diverse sources for hidden trends and meaningful insights.
• Build scalable pipelines for data ingestion, transformation, and quality control.
• Apply ontology mapping, terminology harmonization, and metadata strategies across MedDRA, WHO Drug, and related standards.
• Ensure robust data lineage, traceability, and audit readiness.
• Support modernization of pharmacovigilance and organovigilance systems through AI‑enabled automation and decision support tools.
• Improve case processing efficiency, medical review, and governance reporting via AI‑enabled solutions.
• Contribute to next‑generation surveillance models for novel modalities such as xenotransplantation, cell therapy, gene therapy, and organ‑based therapeutics.
• Develop AI‑enabled dashboards and visualization tools for rapid interpretation of safety trends.
• Ensure AI models and digital tools align with GxP, privacy, security, validation, and regulatory expectations.
• Support model governance including performance monitoring, unbiased detection, explainability (XAI), and change control.
• Maintain documentation for validation, testing, intended use, and lifecycle management.
• Collaborate with safety, clinical, regulatory, medical affairs, biostatistics, and IT teams.
• Provide technical guidance to analysts, data scientists, and business partners.
• Deliver validated AI solutions that create measurable gains in vigilance quality, speed, and insight generation.
• Improve detection and prioritization of safety signals through advanced analytics.
• Enhance case processing and review efficiency while preserving quality and compliance.
• Establish reliable, scalable biomedical data assets for future innovation.
• Maintain regulatory‑ready governance for AI‑enabled safety systems.
• Advance our leadership position in responsible AI for the future of medicine.
• Perform other duties as required.

• Bachelor’s, Master’s, or PhD in computer science, engineering, applied mathematics, data science, biomedical engineering, bioinformatics, artificial intelligence, or related field, with the following experience: 8+ years with a Bachelor’s, 6+ years with a Master’s, or 2+ years post‑PhD.
• 5+ years of experience in AI engineering, machine learning, or advanced…