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About This Role
Position Overview
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The Senior Director of Applied AI Product leads the organization's applied AI product function, spanning product strategy, discovery, delivery, and the roadmap for AI\-powered customer\-facing capabilities. This role sits at the intersection of product leadership and applied AI, and is accountable for how AI experiences are envisioned, shaped, and delivered into products that create measurable value for customers and the business.
Key Responsibilities
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### Product Strategy \& Vision
- Identify opportunities to apply AI across the product portfolio and customer workflows
- Translate organizational objectives into phased, deliverable AI product initiatives
- Communicate product vision, roadmap progress, and tradeoffs to executive stakeholders
### AI Product Discovery \& Definition
- Lead discovery to validate AI use cases against real customer problems and value
- Define product requirements, success criteria, and acceptance thresholds for AI features
- Partner with AI Engineering and Data to assess feasibility and model readiness
- Prioritize the backlog based on customer impact, effort, and strategic fit
### Applied AI Delivery
- Own end\-to\-end delivery of AI product features from concept to general availability
- Design human\-in\-the\-loop experiences, guardrails, and fallback behaviors for AI features
- Define evaluation criteria and quality bars for model\-driven product experiences
- Drive iterative launches, experimentation, and continuous improvement post\-release
### Cross\-Functional Leadership
- Partner with Engineering, Data, and Commercial teams to ship AI capabilities
- Align product, GTM, and delivery timelines across dependent teams and stakeholders
- Coordinate build vs. buy decisions for AI product capabilities with architecture teams
- Serve as the connective tissue between technical AI work and customer\-facing outcomes
### Customer \& Market Engagement
- Engage customers and prospects to surface needs, validate concepts, and gather feedback
- Monitor the competitive and market landscape for emerging applied AI capabilities
- Support sales, marketing, and customer success with positioning and enablement for AI products
- Translate customer signals into roadmap priorities and product improvements
### Product Leadership
- Hire, develop, and retain a high\-performing AI product management team
- Establish product standards: discovery rigor, spec quality, metrics definition, and launch readiness
- Foster a culture of customer\-centricity, iterative experimentation, and responsible AI practices
- Collaborate cross\-functionally with Engineering, Design, Data Science, Security, and Legal teams
### Responsible AI \& Product Governance
- Embed responsible AI principles covering fairness, transparency, and explainability into products
- Ensure AI features comply with applicable data privacy regulations (HIPAA, GDPR, CCPA as relevant)
Qualifications
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### Required
- 10\+ years in product management, with a demonstrable track record in senior AI or data product leadership
- Experience taking AI\-powered products from discovery to production at scale
- Hands\-on experience partnering with engineering and data science teams on ML/LLM\-driven features
- Strong communication skills; able to present product strategy to executive and non\-technical audiences
### Preferred
- Familiarity with LLM product patterns (RAG, agents, guardrails, human\-in\-the\-loop design)
- Familiarity with AI governance standards (NIST AI RMF, EU AI Act, SOC 2 AI controls)
- Background in the healthcare industry
- Prior experience defining evaluation frameworks and quality metrics for AI product experiences
Success Metrics
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- Applied AI product roadmap published and approved by executive leadership within 90 days
- AI product features shipped to general availability against committed timelines
- Product discovery and evaluation framework for AI features in place within 6 months
- Adoption and customer value targets defined and met for launched AI capabilities
Base salary for the role is commensurate with experience and can range between $200,000 \- 250,000 \+ annual bonus opportunity.
### Hiring Locations
Our main office is located in Center City, Philadelphia, where we operate on a hybrid model with in\-office work required three days a week for local employees. We believe collaboration is most effective when teams come together, which is why we prioritize hiring in the Philadelphia area.
For certain roles, we also hire from hub locations—regions where we have an established presence with multiple team members working remotely. While these employees primarily work from home, we bring them together in person at least once a year for team\-building, collaboration, and strategic planning.
Due to tax and labor regulations, we can only hire from specific states. Remote work is supported in the following key hub locations and approved states:
Hub Locations:
- Philadelphia, Pennsylvania
- Boston, Massachusetts
- New York City, New York
- Baltimore, Maryland
- Washington, D.C.
- Charlotte, North Carolina
- Raleigh\-Durham, North Carolina
- Atlanta, Georgia
- Chicago, Illinois
Approved States for Remote Work:
CT, DE, FL, GA, IL, IN, MA, MD, MI, NC, NJ, NY, OH, PA, TN, and VA.
About HealthVerity
HealthVerity is the leader in privacy\-protected real\-world data exchange, transforming how healthcare and life sciences organizations connect and analyze disparate patient data. By enabling access to the industry's largest RWD ecosystem, HealthVerity supports critical applications in clinical development, commercial strategy, regulatory decision\-making, and public health. To learn more about HealthVerity, visit healthverity.com.
Why you'll love working here
We are making a difference – Our technology is at the forefront of some of the biggest healthcare challenges in the world.
We are one team – Our people define our culture and always will. We take time out to celebrate each other, and acknowledge the value that each of us adds towards our greater mission. Come share all you have to offer.
We are learners – Every team member is continually learning, no matter if we've been in a role for one year or much longer. We are committed to learning and implementing what is best for our clients, partners, and each other.
Benefits \& Perks
Our benefits package is thoughtfully designed to support and enrich the experience of our full\-time employees, with eligibility limited to those in permanent positions.
- Compensation: competitive base salary \& annual bonus opportunity (for non\-commissioned roles)
- Benefits: We offer health, dental, and vision coverage starting on day 1\. We also offer a 401(k) plan and an equity program, with new hire equity grants beginning at the Director level and above.
- Flexible location: Remote workdays and 3 days a week of in\-office collaboration for team members in the Philadelphia area. Check location requirements with the recruiting team.
- Generous PTO: Take time off as needed, targeted at 4 weeks per year, including vacation, personal and sick time, plus paid parental leave.
- Parental Leave: 12 weeks paid leave for childbearing, surrogacy, and adoption; 6 weeks for non\-childbearing parents.
- Comprehensive and individualized onboarding: mentorship program, departmental talks, and a library of resources are available beginning day 1 for each new team member to minimize the stress of starting a new job
- Professional development: biweekly 1:1s, hands\-on leadership that is goal\-and growth\-oriented for each team member, and an annual budget to support professional development pursuits
We believe incorporating different ideas, perspectives and backgrounds make us stronger and encourages an environment where ageism, racism, sexism, ableism, homophobia, transphobia or any other form of discrimination are not tolerated. All qualified job applicants will be given consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or on the basis of disability. At HealthVerity, we're working towards an innovative and connected future for healthcare data and believe the future is better together. We can only do that if everyone has a seat at the table.
If you require a reasonable accommodation in completing this application, interviewing, completing any pre\-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to [email protected]
Remote opportunities are not available in all areas and require team members to work from a fixed location due to tax and labor law implications \- specific questions about remote positions can be discussed during the interview process with your recruiter.
Salary Context
This $200K-$250K 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 HealthVerity, 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. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $200K to $250K.
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.
HealthVerity AI Hiring
HealthVerity has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in King of Prussia, PA, US. Compensation range: $250K - $250K.
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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