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About This Role
California, US residents .
The job details are as follows:
Who We Are
We are the first publicly\-traded biotech or pharmaceutical company to take the form of a public benefit corporation. Our public benefit purpose is to provide a brighter future for patients through the development of novel pharmaceutical therapies; and technologies that expand the availability of transplantable organs.
United Therapeutics (Nasdaq: UTHR) seeks to travel down the corridors of indifference to develop treatments for rare, deadly diseases. We were founded in 1996 by a family seeking a cure for their daughter’s pulmonary arterial hypertension (PAH). Today, we have six FDA\-approved therapies that treat PAH, pulmonary hypertension associated with interstitial lung disease (PH\-ILD) and neuroblastoma, a rare pediatric cancer. Our near\-term pipeline seeks to develop additional therapies for PAH and pulmonary fibrosis (PF).
The cure for end\-stage life\-threatening diseases like PAH, PH\-ILD, PF, and many others is an organ transplant, but only a small percentage of donated organs are available to address the vast need. For this reason, we are working to create manufactured organs to address the shortage of kidneys, hearts, lungs, and livers available for transplant. We believe an unlimited supply of tolerable, transplantable organs will eliminate the transplant waiting list and cure end\-stage organ diseases for which transplant is not currently an option.
Who You Are
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.
- 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
Minimum 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 and
- 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
Preferred Qualifications
- 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
Job Location
This position will be located at our Durham, NC office with a hybrid schedule of 4 days in office and the option to work 1 day each week from home. In office requirements could change based on business needs.
At United Therapeutics, our mission and vision are one. We use our enthusiasm, creativity, and persistence to innovate for the unmet medical needs of our patients and to benefit our other stakeholders. We are bold and unconventional. We have fun, we do good.
Eligible employees may participate in the Company’s comprehensive benefits suite of programs, including medical / dental / vision / prescription coverage, employee wellness resources, savings plans (401k and ESPP), paid time off \& paid parental leave benefits, disability benefits, and more. For additional information on Company benefits, please visit https://www.unither.com/careers/benefits\-and\-amenities
United Therapeutics Corporation is an Equal Opportunity Employer, including veterans and individuals with disabilities.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At United Therapeutics, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
United Therapeutics AI Hiring
United Therapeutics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Research Triangle Park, NC, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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