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About This Role
JOB DESCRIPTION
HealthPartners is hiring a Senior Director, Artificial Intelligence and Automation. The Senior Director of AI and Automation drives the enterprise\-wide strategy, development, and deployment of artificial intelligence (AI) and automation solutions across the organization’s health insurance, care delivery, and research domains. This leader champions integrated technologies that enhance member and patient outcomes, streamline operations, accelerate scientific discovery, and uphold responsible AI governance. Reporting to the Data \& Analytics executive, the Senior Director serves as a strategic connector—linking executive leadership, operational and clinical teams, and research stakeholders. This role bridges system\-wide priorities while delivering scalable, ethical, and high\-impact AI and automation capabilities.Required Qualifications:
- Bachelor’s degree in health management, statistics, mathematics, finance, business, and science or other relevant program or equivalent experience.
- Ten (10\) years of leadership experience in AI, machine learning (ML), data science, or automation, with demonstrated success in complex organizations.
- Experience deploying conversational or agentic AI solutions; Proven record of deploying enterprise\-scale AI/automation solutions.
- Knowledge of cloud and data platforms (Azure, Databricks, AWS, GCP) and AI/ML toolkits (TensorFlow, PyTorch, HuggingFace, etc.).
- Experience with automation platforms (UiPath, Blue Prism, Power Automate).
- Expertise in data governance, privacy, and compliance frameworks across payer, provider, and research domains.
- Exceptional communication and change leadership skills, with the ability to bridge executive, clinician, and researcher perspectives.
Preferred Qualifications:
- Advanced degree in Computer Science, Data Science, Engineering, or related field.
- Prior experience in an integrated healthsystem or highly regulated entity.
- Familiarity and experience working within Microsoft Azure and /or Data Bricks
- Familiarity with Fast Healthcare Interoperability Resources (FHIR) standards, EMR data (Epic), payer datasets, and research data platforms.
- Demonstrated ability to deliver measurable impact on cost, quality, and innovation outcomes.
Hours/Location:
- Monday – Friday; core business hours
- This position is primarily on\-site at our Bloomington corporate office, with an expectation of being in the office at least four days per week—typically Monday through Thursday.
- The role includes occasional travel to regional sites (e.g., St. Paul, St. Louis Park) for training sessions and presentations.
Responsibilities:
Execution \& Delivery (25%)
- Oversee the design, development, purchase, and/or deployment of AI models (agentic, predictive, generative, NLP, multimodal) and automation solutions across insurance, clinical, and research workflows.
- Ensure seamless integration of AI/automation into enterprise systems, including EHR (Epic), claims platforms, member portals, research databases, and enterprise data lakes.
- Lead efforts in robotic process automation (RPA), intelligent document processing, and clinical workflow optimization.
- Champion re\-use of AI models across domains (e.g., member risk stratification models leveraged for both care management and clinical trial recruitment).
- Lead a team that will function as an enterprise center of excellence in AI and automation (assisting various technical scrum teams with build vs. buy decisions, implementations, and ongoing monitoring).
Governance, Compliance \& Ethics (25%)
- Enforce and maintain standards for responsible AI, ensuring transparency, fairness, explainability, and bias mitigation.
- Partner with compliance, privacy, and security teams to ensure adherence to HIPAA, CMS, ONC, FDA, and research ethics requirements.
- Provide education and training across clinical, insurance, and research teams on safe use of AI, including PHI handling and liability risks.
Innovation \& External Engagement (25%)
- Support translational research by bringing innovative AI into clinical practice and payer operations.
- Build and measure enterprise AI/automation ROI frameworks, quantifying improvements in patient outcomes, affordability, research productivity, and member satisfaction.
- Function as a visible champion for innovation, balancing experimentation with responsible scaling of proven solutions.
- Foster partnerships with affinity groups, academic institutions, technology vendors, and consortia to advance AI innovation.
Enterprise Strategy \& Leadership (25%)
- Iterate and drive a unified AI and automation strategy that supports all three organizational domains:
+ Health Plan Financing – affordability, risk adjustment, claims automation, customer service, fraud/waste/abuse detection.
+ Care Delivery – clinical decision support, patient engagement, operational efficiency, workforce augmentation.
+ Research – natural language processing for clinical notes, predictive modeling, data enrichment for trials, and research automation.
- Build and lead a multidisciplinary “center of excellence” team of data scientists, machine learning engineers, automation specialists, and applied researchers; inspire a culture of collaboration and support.
- Serve as a system\-wide thought leader on AI adoption, ensuring alignment with strategic priorities of the plan, care group, and institute.
- Support and consult on our technology teams’ AI deployments to ensure safe, equitable, and compliant adoption of AI across the enterprise.
- *Job description rankings/percentages are intended to reflect normal averages over an extended period of time and are subject to daily variances. Quality and efficiency standards should at no time be compromised to meet the average expectations expressed above. Job descriptions are subject to change to accommodate organization or department needs.*
ABOUT US
At HealthPartners we believe in the power of good – good deeds and good people working together. As part of our team, you’ll find an inclusive environment that encourages new ways of thinking, celebrates differences, and recognizes hard work.
We’re a nonprofit, integrated health care organization, providing health insurance in six states and high\-quality care at more than 90 locations, including hospitals and clinics in Minnesota and Wisconsin. We bring together research and education through HealthPartners Institute, training medical professionals across the region and conducting innovative research that improve lives around the world.
At HealthPartners, everyone is welcome, included and valued. We’re working together to increase diversity and inclusion in our workplace, advance health equity in care and coverage, and partner with the community as advocates for change.
Benefits Designed to Support Your Total Health
As a HealthPartners colleague, we’re committed to nurturing your diverse talents, valuing your dedication, and supporting your work\-life balance. We offer a comprehensive range of benefits to support every aspect of your life, including health, time off, retirement planning, and continuous learning opportunities. Our goal is to help you thrive physically, mentally, emotionally, and financially, so you can continue delivering exceptional care.
Join us in our mission to improve the health and well\-being of our patients, members, and communities.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant because of race, color, sex, age, national origin, religion, sexual orientation, gender identify, status as a veteran and basis of disability or any other federal, state or local protected class.
JOB INFO
Job Identification
114775
Organization
HealthPartners/GHI, HealthPartners Enterprise
Posting Date
08/07/2026, 03:17 PM
Locations
8170 33rd Ave S \- Bloomington
Work Schedule
+ Monday – Friday; core business hours
+ This position is primarily on\-site at our Bloomington corporate office, with an expectation of being in the office at least four days per week—typically Monday through Thursday.
+ The role includes occasional travel to regional sites (e.g., St. Paul, St. Louis Park) for training sessions and presentations.
Hours Per Week/FTE
40 hrs weekly / 1\.0 FTE
Job Shift
Day
Position Type
Full\-time regular
Job Category
Health Information Management
Department
Inforamtics and Data Ops Mgmt Rollup
Pay Range
$87\.38 \- $135\.44 hourly
Pay Range Statement
Compensation is based on the level and requirements of the role. Pay within our ranges may also be determined by education, experience, knowledge, skills, location, and abilities as well as internal equity. Hired candidates may be eligible to receive additional compensation based on role (e.g., shift differential, bonus, sales incentive, productivity pay, etc.).
Overtime Eligibility Status
Exempt
Worker Type
Employee
Salary Context
This $180K-$280K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At HealthPartners, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($230K) sits 7% above the category median. Disclosed range: $180K to $280K.
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.
HealthPartners AI Hiring
HealthPartners has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in MN, US. Compensation range: $280K - $280K.
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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