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About This Role
Position Description
At UNOS, the data we steward does not just power analytics products. It supports the systems, insights, and decisions that help save lives through organ donation and transplantation.
The Vice President, Data \& AI is a senior technology executive responsible for defining and executing UNOS's enterprise data and artificial intelligence strategy. This role provides leadership across Data Engineering, Analytics Engineering, Data Architecture, Data Governance, Data Products, and AI/ML Engineering, ensuring these capabilities operate as a unified function that advances UNOS's mission and strategic priorities.
The Vice President serves as the executive sponsor for enterprise data and AI initiatives, driving modernization of UNOS's data ecosystem while building future capabilities that leverage advanced analytics to build intelligent products and tools at scale and new data integration pathways, machine learning, and artificial intelligence to improve organizational performance, customer value, and decision\-making.
Reporting directly to the Chief Executive Officer, this role serves as a member of the Technology Leadership Team and works closely with Executive Leadership to align Data \& AI investments with organizational priorities and long\-term strategy.
Key Responsibilities
Strategic Leadership \& Vision
- Define and execute the enterprise Data \& AI strategy, establishing a multi\-year roadmap aligned with organizational goals, technology priorities, and mission outcomes.
- Serve as the executive champion for data as a strategic enterprise asset, promoting practices that improve data quality, accessibility, trust, and business value.
- Partner with Executive Leadership to align Data \& AI investments with organizational priorities, product strategy, operational excellence, and future growth opportunities.
- Advise leaders on emerging trends in healthcare data, interoperability, analytics, artificial intelligence, and technology innovation.
- Establish performance measures that demonstrate the business impact and value realized through Data \& AI initiatives.
- Provide leadership through Directors, Managers, and senior technical leaders across the Data \& AI organization.
Organizational Leadership
- Lead and develop a high\-performing organization spanning Data Engineering, Analytics Engineering, Data Architecture, Data Governance, Data Products, and AI/ML Engineering.
- Establish organizational structures, workforce plans, succession strategies, and leadership development programs that support long\-term business needs.
- Foster a culture of accountability, innovation, collaboration, continuous improvement, and technical excellence.
- Allocate resources across multiple functions to balance operational priorities, modernization efforts, innovation, and strategic initiatives.
- Develop leadership capability throughout the organization and ensure effective management practices at all levels.
Data Platform \& Analytics Strategy
- Lead modernization of UNOS's enterprise data platform through scalable, cloud\-native architecture and data engineering practices.
- Oversee the design, implementation, and governance of enterprise data infrastructure, including data lakes, data warehouses, semantic models, and curated analytical datasets.
- Establish standards for reliability, scalability, observability, security, performance, and maintainability.
- Ensure mission\-critical data assets and analytical platforms effectively support operational, scientific, research, and customer\-facing needs.
- Guide platform strategy, architecture decisions, and technology investments that support future organizational growth and innovation.
AI, Data Products \& Innovation
- Define and lead UNOS's artificial intelligence and machine learning strategy, ensuring alignment with business objectives, customer needs, and regulatory requirements.
- Build and mature AI/ML capabilities, including technology, governance, processes, and talent required to develop and operationalize AI solutions at scale.
- Establish standards and oversight for responsible AI, including transparency, explainability, governance, monitoring, and risk management.
- Evaluate and guide the use of machine learning, predictive analytics, generative AI, and emerging technologies across internal and customer\-facing solutions.
- Partner with Product, Research, Technology, and business leaders to identify opportunities for data\-driven innovation and new capabilities.
- Champion a data product mindset that treats enterprise data assets as strategic products with defined ownership, quality standards, and customer expectations.
Data Governance, Quality \& Compliance
- Serve as the executive authority for enterprise data governance, data stewardship, data quality, and AI oversight.
- Establish organizational policies, standards, and governance frameworks for data management, privacy, security, retention, accessibility, and responsible AI usage.
- Sponsor governance forums that prioritize investments, manage risk, establish accountability, and support enterprise decision making related to data and AI.
- Ensure compliance with HIPAA, data privacy requirements, information security standards, and emerging AI governance expectations.
- Promote privacy\-by\-design, security\-by\-design, and high\-quality data management practices across the organization.
Financial \& External Leadership
- Own the Data \& AI operating budget, including workforce planning, technology investments, vendor management, and long\-term capability development.
- Develop and oversee multi\-year investment roadmaps supporting data, analytics, governance, and artificial intelligence capabilities.
- Establish value realization measures and prioritize investments to maximize organizational impact and return on investment.
- Lead strategic vendor relationships and technology partnerships supporting the Data \& AI ecosystem.
- Represent UNOS with healthcare partners, researchers, government agencies, technology vendors, and industry groups on matters related to data, analytics, interoperability, and artificial intelligence.
Minimum Requirements
- 15\+ years of progressive experience in data, analytics, technology, engineering, artificial intelligence, machine learning, or related disciplines.
- 7\+ years of experience leading managers, senior technical leaders, and multi\-disciplinary organizations.
Critical Skills
- Demonstrated success leading enterprise\-scale data modernization, analytics, governance, cloud transformation, or AI initiatives.
- Experience managing significant technology investments, vendor relationships, and complex organizational initiatives.
- Proven ability to influence executive leadership and drive strategy through data, analytics, and technology capabilities.
- Experience building and leading organizations across multiple technical disciplines, including engineering, architecture, analytics, governance, and AI/ML functions.
- Expertise with modern cloud data platforms including Azure, Databricks, Azure Data Lake, Azure Synapse, Azure Data Factory, and related technologies.
- Strong understanding of data architecture, data modeling, data warehousing, ELT/ETL design, metadata management, and enterprise data operations.
- Experience building and leading large\-scale data and analytics platforms supporting both operational and customer\-facing solutions.
- Strong knowledge of AI/ML technologies, MLOps, model governance, model deployment, and large language model technologies.
- Deep understanding of data governance, data stewardship, privacy requirements, HIPAA, security controls, and responsible AI practices.
- Ability to establish governance frameworks, operating models, and organizational strategies that improve accountability, scalability, and business value.
- Exceptional executive communication, stakeholder management, and influence skills.
- Ability to translate complex technical concepts into business\-focused recommendations for executive and board\-level audiences.
- Familiarity with healthcare interoperability standards including HL7v2, FHIR, QHIN, or similar healthcare data exchange models.
- Experience supporting clinical, scientific, research, or highly regulated data environments preferred.
Education
- Bachelor’s degree in computer science, Data Science, Information Systems, Engineering, Analytics, Artificial Intelligence, Healthcare Informatics, or a related field required.
+ Master's degree in a related discipline preferred.
- Formal coursework or concentration in data architecture, distributed systems, cloud computing, machine learning, or AI is a plus.
Salary Context
This $201K-$291K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At United Network For Organ Sharing, 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($246K) sits 13% above the category median. Disclosed range: $201K to $291K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
United Network For Organ Sharing AI Hiring
United Network For Organ Sharing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Richmond, VA, US. Compensation range: $291K - $291K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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