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JOB DESCRIPTION Job Summary Leads the executive strategy development, vision and direction for the activation of Molina’s enterprise Artificial Intelligence (AI) governance & vendor oversight strategy and operating framework, responsible for AI vendor oversight, regulatory compliance, risk management, model governance, and value protection across the AI lifecycle. In partnership with technology and business leaders, establishes clear decision rights, controls, and monitoring practices that enable Molina to scale vended AI solutions responsibly, securely, and consistently across the organization.
Essential Job Duties
- Leads Molina’s enterprise AI vendor governance strategy and operating framework, including governance bodies, decision rights, approval pathways, policies, standards, controls, and guardrails for the responsible use and adoption of AI tools, models, processes, and technological enhancements across the enterprise.
- Develops and maintains enterprise policies and standards for responsible AI, model risk management, data privacy, cybersecurity, regulatory compliance, explainability, human oversight, documentation, and ongoing monitoring.
- Leads risk-based intake, review, and approval processes for AI use cases and vended solutions across the AI lifecycle, from ideation and development through deployment, operation, material change, and retirement.
- Establishes and maintains Molina’s enterprise AI inventory, risk classification methodology, control library, evidence requirements, and performance-monitoring mechanisms.
- Defines escalation protocols and accountability for AI risks, control gaps, incidents, exceptions, and remediation, including recommendations to pause, restrict, modify, or retire AI solutions when risk thresholds are exceeded.
- Chairs or provides executive leadership to AI governance and approval committees, ensuring timely, transparent, and documented decisions aligned with enterprise priorities and risk appetite.
- Partners with Legal, Compliance, Privacy, Information Security, Internal Audit, Enterprise Risk Management, Technology, Clinical, Operations, and business leaders to integrate AI governance into existing enterprise risk and control frameworks.
- Ensures AI vended solutions comply with applicable CMS, state, healthcare, privacy, security, and contractual requirements, with specific consideration for PHI, PII, clinical and coverage decision support, member communications, and other high-impact managed care use cases.
- Leads governance and oversight for third-party AI products and services, including vendor due diligence, contracting requirements, risk assessments, control validation, performance monitoring, and exit or contingency planning.
- Establishes executive reporting and metrics that provide visibility into AI portfolio risk, control effectiveness, regulatory readiness, incidents, remediation, and responsible AI adoption.
- Collaborates with Molina’s AI Institutional, Retail, Middleware, Engineering, and business value-stream leaders to embed governance requirements into solution design, implementation planning, change management, vended solutions and value realization.
- Represents Molina in external AI governance, regulatory, industry, and vendor forums; monitors emerging laws, standards, and practices and translates changes into actionable enterprise requirements.
- Builds and develops a high-performing AI governance and oversight capabilities, including team leadership, role clarity, governance education, and enterprise-wide awareness of responsible AI expectations.
Required Qualifications
- At least 12 years of progressive experience in managed care, governance, compliance, enterprise risk management, technology risk, privacy, information security, audit, healthcare operations, or equivalent combination of relevant education and experience.
- At least 7 years of management or leadership experience.
- Demonstrated experience establishing or leading governance frameworks for artificial intelligence, emerging technology, data, models, or other high-impact enterprise capabilities in regulated environments.
- Strong knowledge of healthcare regulatory and compliance requirements, enterprise risk management, model risk, data privacy, cybersecurity, and third-party risk management.
- Experience designing policies, standards, controls, risk assessments, approval processes, monitoring mechanisms, and executive reporting for complex enterprise programs.
- Conceptual understanding of AI and machine learning technologies and the solution lifecycle, from ideation and development through deployment, monitoring, and retirement.
- Strong understanding of responsible AI principles, including fairness, transparency, explainability, accountability, human oversight, privacy, security, and reliability.
- Demonstrated ability to translate evolving regulatory requirements and technical risks into practical business controls and operating practices.
- Strong executive communication, cross-functional collaboration, stakeholder management, and influence skills in a matrixed environment.
- Demonstrated ability to lead, mentor, and develop high-performing teams and to drive initiatives from concepts through implementation and sustained operation.
- Experience assessing third-party AI products, cloud-based AI services, large language models, generative AI, agentic AI, machine learning, or automated decision-support systems.
- Experience operating with ambiguity and balancing innovation, member and patient impact, regulatory obligations, operational feasibility, and business value.
Preferred Qualifications
- Master’s degree in business administration, Healthcare Administration, Information Technology, Data Science, Law, Public Policy, Risk Management, or related field.
- Experience in health insurance, managed care, Medicare, Medicaid, Marketplace, clinical operations, or other highly regulated healthcare environments.
- Experience with AI governance frameworks, model risk management standards, privacy impact assessments, security risk assessments, regulatory readiness, or audit support.
- Relevant certification in risk, privacy, security, compliance, audit, governance, or artificial intelligence.
To all current Molina employees: If you are interested in applying for this position, please apply through the Internal Job Board.
Molina Healthcare offers a competitive benefits and compensation package. Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V
Pay Range: $214,131 - $417,556 / ANNUAL
- Actual compensation may vary from posting based on geographic location, work experience, education and/or skill level.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Molina Healthcare, 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 $212,040 based on 13,780 positions with disclosed compensation.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Molina Healthcare AI Hiring
Molina Healthcare has 1 open AI role right now. They're hiring across AI/ML Engineer.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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