Interested in this AI/ML Engineer role at Capital Health?
Apply Now →About This Role
Capital Health is the region's leader in providing progressive, quality patient care with significant investments in our exceptional physicians, nurses and staff, as well as advanced technology. Capital Health is a dynamic health care resource accredited by the DNV that includes two hospitals, an outpatient center, satellite ED, and an expansive network of primary and specialty care. Capital Health Medical Group is made up of more than 600 physicians and other providers who offer primary and specialty care, as well as hospital\-based services, to patients throughout the region.
Capital Health recognizes that attracting the best talent is key to our strategy and success as an organization. As a result, we aim for flexibility in structuring competitive compensation offers to ensure we can attract the best candidates.
The listed pay range or pay rate reflects compensation for a full\-time equivalent (1\.0 FTE)position. Actual compensation may differ depending on assigned hours and position status (e.g., part\-time).
Scheduled Weekly Hours:
40
Position Overview
SUMMARY (Basic Purpose of the Job)
The Director, Product Engineering \& AI is a strategic and operational technology leader responsible for advancing Capital Health’s internal product engineering, AI\-enabled workflow transformation, and enterprise automation initiatives. This role leads the design, development, integration, and operationalization of internally developed digital solutions that improve clinical, operational, financial, and patient experience outcomes across the organization. The Director is responsible for establishing a modern product engineering operating model that combines software engineering, AI technologies, workflow transformation, and enterprise integration principles to deliver scalable and sustainable solutions aligned with organizational priorities. Partners closely with operational, clinical, analytics, infrastructure, and security leaders to identify high\-value opportunities for automation, operational intelligence, and AI\-enabled transformation. Balances strategic leadership with practical execution and is expected to build a high\-performing engineering culture focused on agility, accountability, scalability, security, and measurable business value.
MINIMUM REQUIREMENTS
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Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or related field required. Master’s degree in Healthcare Informatics, Data Analytics, AI, or Business Administration preferred.
Experience: Eight years of progressive experience in software engineering, enterprise applications, digital transformation, or healthcare technology leadership. Five years leadership experience managing technical engineering, application development, or digital product teams. Experience leading cross\-functional technology initiatives involving enterprise integrations, workflow transformation, or AI\-enabled solutions.
Knowledge and Skills : Strong understanding of modern software engineering principles, APIs, cloud\-native architectures, and enterprise integration strategies. Understanding of AI technologies including large language models, workflow automation, AI\-assisted applications, and orchestration frameworks. Strong operational and workflow transformation mindset with the ability to connect technology capabilities to measurable organizational outcomes. Ability to lead technical teams while balancing strategic priorities, operational realities, and execution discipline. Strong understanding of agile delivery concepts, product development, lifecycle management, and engineering governance principles. Ability to collaborate effectively across clinical, operational, financial, and technical leadership teams. Strong communication and executive presentation skills. Understanding of cybersecurity, secure development principles, identity management, and enterprise governance expectations.
Special Training: Training or certifications in cloud platforms, AI technologies, product management, agile methodologies, healthcare informatics, or enterprise architecture preferred.
Mental, Behavioral and Emotional Abilities: Demonstrates strong strategic thinking and operational decision\-making capabilities. Ability to lead through ambiguity and organizational transformation. Demonstrates accountability, adaptability, and collaborative leadership behaviors. Ability to mentor and grow technical talent while building a culture of innovation, ownership, and continuous improvement. Ability to balance innovation with practical execution and organizational readiness.
ESSENTIAL FUNCTIONS
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- Leads the enterprise Product Engineering \& AI function across Capital Health.
- Develops and operationalizes a scalable internal product engineering model aligned with organizational transformation priorities.
- Leads teams responsible for AI\-enabled applications, workflow automation, enterprise integrations, operational tooling, and internally developed digital platforms.
- Partners with operational and clinical leaders to identify high\-value workflow transformation opportunities.
- Establishes engineering standards, architectural governance, development practices, and secure software lifecycle processes.
- Oversees the prioritization, planning, execution, and operationalization of internally developed products and automation initiatives.
- Collaborates closely with Infrastructure, Security, Analytics, Clinical Applications, Revenue Cycle, and Enterprise Operations teams.
- Drives responsible adoption of AI technologies while ensuring alignment with organizational governance, compliance, privacy, and cybersecurity expectations.
- Establishes measurable success metrics and value realization processes for transformation initiatives.
- Mentors and develops engineering and technical staff capabilities.
PHYSICAL DEMANDS AND WORK ENVIRONMENT
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- Frequent physical demands include: Standing , Walking , Reaching forward , Reaching overhead
- Occasional physical demands include: Climbing (e.g., stairs or ladders) , Carry objects , Push/Pull , Twisting , Bending , Squat/kneel/crawl , Taste or Smell
- Continuous physical demands include: Sitting , Wrist position deviation , Pinching/fine motor activities , Keyboard use/repetitive motion , Talk or Hear
- Lifting Floor to Waist 15 lbs. Lifting Waist Level and Above 10 lbs.
- Sensory Requirements include: Accurate Near Vision, Accurate Far Vision, Accurate Depth Perception, Accurate Hearing
- Anticipated Occupational Exposure Risks Include the following: N/A
This position is eligible for the following benefits:
- Medical Plan
- Prescription drug coverage \& In\-House Employee Pharmacy
- Dental Plan
- Vision Plan
- Flexible Spending Account (FSA)
- Healthcare FSA
- Dependent Care FSA
- Retirement Savings and Investment Plan
- Basic Group Term Life and Accidental Death \& Dismemberment (AD\&D) Insurance
- Supplemental Group Term Life \& Accidental Death \& Dismemberment Insurance
- Disability Benefits – Long Term Disability (LTD)
- Disability Benefits – Short Term Disability (STD)
- Employee Assistance Program
- Commuter Transit
- Commuter Parking
- Supplemental Life Insurance
- Voluntary Life Spouse
- Voluntary Life Employee
- Voluntary Life Child
- Voluntary Legal Services
- Voluntary Accident, Critical Illness and Hospital Indemnity Insurance
- Voluntary Identity Theft Insurance
- Voluntary Pet Insurance
- Paid Time\-Off Program
The pay range listed is a good faith determination of potential base compensation that may be offered to a successful applicant for this position at the time of this job advertisement and may be modified in the future. When determining base salary and/or rate, several factors may be considered including, but not limited to location, years of relevant experience, education, credentials, negotiated contracts, budget, market data, and internal equity. Bonus and/or incentive eligibility are determined by role and level.
The salary applies specifically to the position being advertised and does not include potential bonuses, incentive compensation, differential pay or other forms of compensation, compensation allowance, or benefits health or welfare. Actual total compensation may vary based on factors such as experience, skills, qualifications, and other relevant criteria.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Capital Health, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Capital Health AI Hiring
Capital Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pennington, NJ, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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