Interested in this AI/ML Engineer role at Presbyterian Healthcare Services?
Apply Now →About This Role
Location Address:
9521 San Mateo NE Albuquerque, NM 87113\-2237
Summary:
Presbyterian Health Plan (PHP) seeks a visionary technology executive to serve as its next Executive Director of AI Engineering \& Process Automation. This highly strategic leadership role will define and operationalize the future of artificial intelligence, intelligent automation, and digital transformation across the Health Plan.
As healthcare organizations face mounting pressure to improve efficiency, reduce administrative burden, accelerate decision\-making, and enhance member and provider experiences, Presbyterian is making significant investments in AI and automation capabilities. The Executive Director will lead the strategy, engineering delivery, governance, and adoption of enterprise AI and automation solutions that drive measurable clinical, operational, financial, and customer experience outcomes.
This executive will build and lead a multidisciplinary team responsible for delivering secure, compliant, and scalable automation and AI capabilities, including robotic process automation (RPA), machine learning, workflow automation, predictive analytics, and generative AI solutions. Working closely with operational leaders across Claims, Prior Authorization, Utilization Management, Provider Operations, Enrollment, Finance, Care Management, and Member Services, the Executive Director will identify and prioritize transformation opportunities and ensure successful adoption from concept through enterprise deployment.
The ideal candidate is a transformational technology leader who combines deep engineering expertise with strong business acumen and a proven track record of delivering AI and automation solutions at scale.
Work Arrangement:
- Remote: Open to applicants in the United States, excluding CA, IL, ND, NY, OH, WA, and WY.
- Hybrid (Strongly Preferred): For individuals within 60 miles of Albuquerque, in\-office presence is required Tuesday through Thursday.
Job Description:
Strategic AI \& Automation Leadership
- Develop and execute the Health Plan's AI and automation strategy, aligning investments with organizational priorities and measurable business outcomes.
- Establish a multi\-year roadmap for AI, machine learning, generative AI, workflow automation, and robotic process automation (RPA).
- Identify opportunities to improve administrative efficiency, operational performance, member experience, provider experience, and cost management through intelligent automation.
- Prioritize initiatives based on business value, implementation complexity, risk, and return on investment.
- Serve as a strategic advisor to executive leadership regarding emerging AI technologies, market trends, and innovation opportunities.
AI Engineering \& Solution Delivery
- Lead the design, development, deployment, and scaling of production\-ready AI and automation solutions.
- Oversee implementation of automation and AI initiatives across Claims, Prior Authorization, Utilization Management, Provider Operations, Finance, Enrollment, Care Management, Encounters, and Member Services.
- Establish engineering standards, development methodologies, and quality controls that ensure scalable and sustainable delivery.
- Create repeatable frameworks for evaluating, testing, and operationalizing emerging technologies.
Architecture, Platforms \& Technology Governance
- Define enterprise architecture standards supporting AI and automation initiatives.
- Establish integration patterns, APIs, data pipelines, orchestration frameworks, observability capabilities, and resiliency standards.
- Lead platform evaluation, selection, implementation, and lifecycle management activities.
- Ensure alignment with enterprise technology strategy and long\-term scalability goals.
Responsible AI, Compliance \& Risk Management
- Establish enterprise governance frameworks for responsible AI usage.
- Ensure compliance with HIPAA, privacy regulations, security requirements, and organizational policies.
- Implement controls supporting explainability, auditability, model lifecycle management, drift monitoring, and bias mitigation.
- Partner with Compliance, Legal, Risk Management, Privacy, and Security teams to maintain appropriate oversight.
Operational Excellence \& Value Realization
- Establish operational support models for AI and automation solutions, including SLAs, incident management, monitoring, release governance, and continuous improvement.
- Develop performance dashboards and executive reporting frameworks.
- Measure and communicate business outcomes associated with AI investments.
- Drive optimization and continuous improvement of deployed solutions.
Executive Partnership \& Change Leadership
- Partner closely with operational leaders to identify automation opportunities and drive organizational adoption.
- Lead executive discussions regarding priorities, investments, risks, tradeoffs, and expected outcomes.
- Develop change management strategies supporting implementation and user adoption.
- Translate technical concepts into actionable business recommendations for executive audiences.
Talent Leadership \& Organizational Capability Building
- Build, lead, and develop a high\-performing team of AI engineers, automation developers, data engineers, solution architects, and technical leaders.
- Foster a culture of innovation, accountability, continuous learning, and collaboration.
- Develop succession plans and technical career pathways.
- Elevate organizational capabilities in AI engineering, automation, and digital transformation.
Success Measures
Within the first 12–24 months, the Executive Director will be expected to:
- Develop and gain executive alignment on a comprehensive AI and automation roadmap aligned with Health Plan priorities.
- Establish a sustainable AI Engineering \& Process Automation function with strong governance, architecture standards, and operational processes.
- Successfully deploy multiple production AI and automation solutions that deliver measurable operational and financial improvements.
- Reduce administrative burden, manual work effort, claim pend volumes, turnaround times, and operational inefficiencies across targeted functions.
- Implement responsible AI governance frameworks that ensure compliance with security, privacy, regulatory, and ethical standards.
- Deliver measurable value realization through improvements in productivity, accuracy, cost avoidance, quality, and member/provider experience.
- Build executive dashboards and reporting frameworks that demonstrate adoption, performance, ROI, and business impact.
- Recruit, develop, and retain a high\-performing team capable of scaling AI and automation capabilities across the enterprise.
- Establish trusted advisory relationships with operational and executive leaders and become the organization's recognized leader in AI\-driven transformation.
- Position Presbyterian Health Plan as a leader in intelligent automation and responsible AI adoption within the healthcare payer industry.
Additional Job Description:
- 12\+ years of progressive technology leadership experience in software engineering, AI, automation, digital transformation, or enterprise technology delivery.
- 7\+ years leading managers, technical leaders, and enterprise\-scale technology programs.
- Demonstrated executive accountability for AI, automation, engineering, or digital transformation initiatives.
- Proven success building and scaling high\-performing engineering, AI, or automation organizations.
- Experience influencing executive stakeholders and leading enterprise transformation initiatives.
- Demonstrated track record establishing strategy and governance while delivering measurable business outcomes.
Benefits
Benefits are effective day\-one (for .45 FTE and above) and include:
- Competitive salaries
- Full medical, dental and vision insurance
- Flexible spending accounts (FSAs)
- Free wellness programs
- Paid time off (PTO)
- Retirement plans, including matching employer contributions
- Continuing education and career development opportunities
- Life insurance and short/long term disability programs
About Us
Presbyterian Healthcare Services is a locally owned, not\-for\-profit healthcare system of nine hospitals, a statewide health plan and a growing multi\-specialty medical group. Founded in New Mexico in 1908, it is the state's largest private employer with approximately 11,000 employees.
Presbyterian's story is really the story of the remarkable people who have chosen to work here. Starting with Reverend Cooper who began our journey in 1908, the hard work of thousands of physicians, employees, board members, and other volunteers brought Presbyterian from a tiny tuberculosis sanatorium to a statewide healthcare system, serving more than 700,000 New Mexicans.
We are part of New Mexico's history \- and committed to its future. That is why we will continue to work just as hard and care just as deeply to serve New Mexico for years to come.
About New Mexico
New Mexico's unique blend of Spanish, Mexican and Native American influences contribute to a culturally rich lifestyle. Add in Albuquerque's International Balloon Fiesta, Los Alamos' nuclear scientists, Roswell's visitors from outer space, and Santa Fe's artists, and you get an eclectic mix of people, places and experiences that make this state great.
Cities in New Mexico are continually ranked among the nation's best places to work and live by Forbes magazine, Kiplinger's Personal Finance, and other corporate and government relocation managers like Worldwide ERC.
New Mexico offers endless recreational opportunities to explore, and enjoy an active lifestyle. Venture off the beaten path, challenge your body in the elements, or open yourself up to the expansive sky. From hiking, golfing and biking to skiing, snowboarding and boating, it's all available among our beautiful wonders of the west.
AA/EOE/VET/DISABLED. PHS is a drug\-free and tobacco\-free employer with smoke free campuses.
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 Presbyterian Healthcare Services, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
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.
Presbyterian Healthcare Services AI Hiring
Presbyterian Healthcare Services has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Albuquerque, NM, US. Compensation range: $124K - $124K.
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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