Interested in this AI/ML Engineer role at Yale New Haven Health?
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Overview:
To be part of our organization, every employee should understand and share in the YNHHS Vision, support our Mission, and live our Values. These values \- integrity, patient\-centered, respect, accountability, and compassion \- must guide what we do, as individuals and professionals, every day.
The Executive Director, DTS, AI \& Automation is responsible for leading the vision, strategy, and execution of enterprise\-wide AI initiatives, ensuring alignment with organizational goals while collaborating with senior leadership across clinical, operational, and finance domains. This role drives AI roadmaps, optimizes workflows through innovative technologies, manages the AI Innovation team, establishes best practices for scalable solutions, oversees resource allocation and milestone definition, and serves as the primary point of contact for AI strategy across the enterprise. Working closely with DTS leadership, business and clinical operations teams, and key stakeholders, this position fosters AI innovation to deliver scalable and impactful solutions that transform organizational efficiency.
EEO/AA/Disability/Veteran
Responsibilities:
- Develops and executes the enterprise\-wide AI vision, strategy, and initiative roadmap, ensuring alignment with organizational goals, security, and business outcomes.
- Serves as the organizational authority and champion for AI, inspire a culture of collaboration, innovation, and support.
- Builds, leads, and mentors a high\-performing AI Innovation team providing strategic direction, professional development, performance management, accountability, and continuous learning.
+ Develops and maintains strategic resource planning models, budget forecasts, and staffing strategies for the organization's AI and automation initiatives.
+ Creates and maintains documentation standards for enterprise AI and automation assets, ensuring knowledge transfer and operational sustainability.
+ Effectively engage and leverage non\-reporting team members, such as data scientists, applied researchers, and clinical/operational subject matter experts.
- Evaluates and prioritizes AI opportunities across the organization, assessing strategic value, resource requirements, and ROI potential to build a comprehensive portfolio factoring business goal alignment, scalability, and innovation.
+ Facilitates Digital/AI Implementation ADvisory (DAIAD) Committee to vet and evaluate AI Use Case Proposals
+ Foster cross\-functional collaboration to identify high\-impact use cases for AI and automation, driving innovation and measurable business outcomes.
+ Partners with business and clinical stakeholders to define, document, and transform critical workflows that deliver significant operational improvements to align with AI strategies, business needs, and ensure seamless integration.
+ Directs and oversees the development of business cases, technical feasibility studies, and cost\-benefit analyses for major AI initiatives, securing funding and resource allocation.
- Evaluates emerging AI technologies and vendor solutions, making strategic recommendations for enterprise adoption and integration.
+ Assist various technical and operational teams with build vs. buy decisions, implementations, and ongoing monitoring.
+ Evaluate, select, and implement solutions that complement and extend AI capabilities.
- Ensure seamless integration of AI into enterprise systems through oversight of change management, training, and post live performance monitoring.
+ Reviews and approves high\-level design specifications for enterprise AI and automation solutions, ensuring architectural alignment, security, scalability, and high\-performance solutions.
+ Lead training and change management initiatives to build organizational readiness for AI adoption.
+ Coordinates trouble\-shooting efforts when issues are identified for programs and systems
- Monitors program performance against strategic KPIs, providing executive\-level reporting on AI and automation outcomes and business value realization.
+ Build and measure enterprise AI and automation ROI frameworks, quantifying improvements in patient outcomes, operational efficiency, and efficacy improvement.
+ Adjusting AI and automation strategies as needed to maximize value and minimize risk.
- Align with existing governance and ethical guidelines for AI adoption ensuring responsible use, transparency, and alignment with organizational policies/values.
- Performs job\-related duties in conformance with DTS standards; may initiate standards.
- Performs all duties and responsibilities in such a manner as to support the "I Am Yale\-New Haven" service excellence program. Performs all duties and responsibilities in such a manner as to support the Vision, Mission, and Values for YNHHS. as well as the department competencies: customer focus, problem solving, innovation, and adaptability. Promotes positive and constructive working relationships within the department.
- Performs other job\-related duties, as required.
Qualifications:
EDUCATION* Bachelor degree in health management, statistics, mathematics, finance, business, science, or other relevant program or equivalent experience. Preferred advanced degree in Computer Science, Data Science, Engineering, or related field.
EXPERIENCE* With a Bachelor's degree, 8 to 10 years of progressive, DTS healthcare related work experience; with at least five (5\) years of AI experience demonstrating business and operations expertise in a leadership role.
SPECIAL SKILLS* Experience leading enterprise\-wide AI and automation initiatives with advanced knowledge of conversational or agentic AI solutions, RPA platforms (UiPath, Blue Prism, Automation Anywhere), and their integration with DevOps methodologies is required.
- Demonstrated expertise in leading AI Centers of Excellence, governing cross\-functional teams, evaluating vendor product fit, and implementing bot deployment.
- Deep understanding of healthcare workflows in both outpatient and inpatient settings, coupled with experience in API management strategies, microservices architectures, and infrastructure\-as\-code approaches.
- Strong technical portfolio management capabilities including version control for AI and automation assets, architectural governance frameworks, and scalability planning across enterprise environments.
- Must possess knowledge of monitoring/alerting systems for automated processes, incident response procedures, and SLA management for critical workflows.
- Ability to evaluate emerging technologies and develop comprehensive technical roadmaps that balance innovation with operational stability while ensuring alignment with organizational objectives.
PHYSICAL DEMAND* Must be willing to travel to all YNHHS delivery networks and practice locations as required for strategic planning, governance meetings, and executive leadership engagement. May require occasional evening or weekend availability to manage critical initiatives or participate in strategic planning sessions. Must be accessible for escalation of critical system issues that impact patient care or business operations.
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 Yale New Haven 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
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.
Yale New Haven Health AI Hiring
Yale New Haven Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Stratford, CT, 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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