Lead AI Engineer, Biomedical & Vigilance Innovation

United Therapeutics•Rtp, NC

3d•Hybrid

About The Position

We are seeking a Lead AI Engineer, Biomedical & Vigilance Innovation with a start-up mindset to drive the design, development, and deployment of advanced AI solutions that transform how biomedical insights are generated and how safety signals are detected, assessed, and acted upon, as well as deploying artificial intelligence solutions that strengthen biomedical research, patient safety, and next-generation vigilance capabilities. This role operates at the intersection of data science, software engineering, and medical safety, applying advanced analytics, machine learning, and automation signal detection, case intelligence, business efficiency, and evidence generation from various sources of scientific and RWD. The position will partner closely with safety physicians, clinical scientists, epidemiologists, regulatory leaders, and technology teams to create scalable platforms that support proactive risk management and accelerate informed decision-making across the product lifecycle. This role is instrumental in shaping the future state of digital vigilance and precision medicine.

Requirements

Bachelor’s Degree in computer science, science, engineering, applied mathematics, data science, biomedical engineering, bioinformatics, artificial intelligence, or related discipline with 8+ years of relevant experience or Master’s Degree in computer science, science, engineering, applied mathematics, data science, biomedical engineering, bioinformatics, artificial intelligence, or related discipline with 6+ years of relevant experience or Doctor of Philosophy (PhD) in computer science, science, engineering, applied mathematics, data science, biomedical engineering, bioinformatics, artificial intelligence, or related discipline with 2+ years of relevant experience
5+ years of experience in AI engineering, machine learning, or advanced analytics within biopharma, healthcare, or regulated industries
5+ years of hands-on expertise in AI tools ( e.g. Python/R/Matlab for ML, TensorFlow/PyTorch, cloud-based ML platforms )
Track record of applying AI, machine learning, and data science to solve hard problems in complex domains not just strategy but working implementations that have delivered measurable outcomes in production environments
Entrepreneurial, transformation-oriented mindset with the ability to move from concept to execution quickly, sustain momentum through ambiguity, and lead organizations through technology-driven change
Background in AI-native product development including agentic AI, LLM-powered applications, autonomous systems, computer vision, or ML-driven process optimization with hands-on experience developing & implementing AI products, not just evaluating them
Familiarity with data architecture, cloud computing, and innovative analytics platforms
Strong problem-solving capability with the ability to operate in complex matrixed environments
Deep fluency in cloud-native engineering, platform architecture, and modern software development practices
Experience with external innovation strategies, academic partnerships, and emerging technology investments that extend organizational capability beyond organic development
Have the engineering mindset to build platforms that are production-grade, compliant, and scalable not just proof-of-concept demonstrations
Communicate technical outputs clearly to non-technical stakeholders and senior leadership
Learn the science quickly and develop enough depth to challenge assumptions, ask the right questions, and identify where technology can create step-change improvements in manufacturing timelines, quality, and cost

Nice To Haves

5+ years of experience with NLP, LLMs, knowledge graphs, or biomedical text mining
Experience with safety systems such as Argus, ArisG, Veeva, or equivalent platforms
Knowledge of pharmacovigilance, clinical development, biomedical data, or healthcare regulations preferred
Exposure to drugs, biologics, devices in rare disease space, or advanced therapeutics
Curiosity about and willingness to develop deep understanding of pharmaceutical domain and automation workflows, advance analytics or benefit risk identification methodologies implementation
Hands-on experience with Pharmacokinetic(PK)/ Pharmacodynamics (PD) modeling and simulation & its applications to AI in Pharmacovigilance
Familiarity with GVP, FDA, EMA, ICH, and data privacy frameworks
Bring the startup urgency to cut through complexity and ship results, while maintaining the regulatory discipline that the PV industry demands
See AI as a practical toolkit knowing when and how to apply machine learning, NLP, computer vision, or agentic systems to specific manufacturing and PV workflows to deliver real operational advantages

Responsibilities

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
Deep understanding & Hands-on experience of know-how of model definitions and fine-tuning models to make them efficient and fit for purpose to UT PV business use cases
Define and execute a bold technology strategy spanning the Global Patient Safety covering usage of AI in day-to-day PV operations, analytical sciences, signal detection, process scale up with a clear mandate to embed AI, machine learning, and agentic automation throughout, within the UT Global Patient Safety Organization
Drive the architecture, development, and delivery of next-generation platforms for pharmacovigilance technological AI initiatives
Integrate structured and unstructured datasets from safety databases, clinical systems, literature sources, real-world evidence, and external repositories
Hand-on experience of creating staging schemas and data mining varied sources to uncover hidden trends & patterns turning into meaningful insights
Build scalable pipelines for ingestion, transformation, and quality control of biomedical data
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 through AI-enabled solutions
Contribute to next-generation surveillance models for novel modalities including xenotransplantation, cell therapy, gene therapy, and organ-based therapeutics
Develop AI enabled dashboards and visualization tools that enable rapid interpretation of safety trends
Ensure AI models and digital tools are developed in alignment with GxP, privacy, security, validation, and regulatory expectations
Support model governance frameworks including performance monitoring, unbiased detection, explainability (XAI), and change control
Maintain documentation for validation, testing, intended use, and lifecycle management
Collaborate with other Safety Functions, Clinical Operations, Regulatory Affairs, Medical Affairs, Biostatistics, and IT functions
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 UTC’s leadership position in responsible AI for the future of medicine
Perform other duties as required