Interested in this AI/ML Engineer role at Mayo Clinic?
Apply Now →About This Role
CityRochester
StateMN
RemoteNO
DepartmentInformation Technology
Why Mayo Clinic
Mayo Clinic is top\-ranked in more specialties than any other care provider according to U.S. News \& World Report. As we work together to put the needs of the patient first, we are also dedicated to our employees, investing in competitive compensation and comprehensive benefit plans – to take care of you and your family, now and in the future. And with continuing education and advancement opportunities at every turn, you can build a long, successful career with Mayo Clinic.
Benefits Highlights
- Medical: Multiple plan options.
- Dental: Delta Dental or reimbursement account for flexible coverage.
- Vision: Affordable plan with national network.
- Pre\-Tax Savings: HSA and FSAs for eligible expenses.
- Retirement: Competitive retirement package to secure your future.
Responsibilities
The Director, AI Governance Technologies is the organizational builder of platforms, tools, and infrastructure that power Mayo's enterprise AI governance program. Supporting the AI Governance and AI Performance Evaluation functions, this role builds and owns the applications and infrastructure that make governance possible at scale, including: an enterprise AI inventory, governance review tooling, reporting infrastructure, pipelines, and tooling, reporting dashboards, detection tooling, and integrations with other Mayo enterprise systems as needed.
The Director also defines technical requirements (format, transport) for product owners to embed governance checkpoints – such as bias assessments, performance and safety thresholds, and documentation requirements – into product development to support electronic post\-deployment reporting to AIA. Finally, this role also defines AI monitoring requirements that will be executed and enforced by product and enterprise IT partners accountable for day\-to\-day operations and life cycle management, and ensures alignment between IT operational home MLOPS functions and AIA governance MLOPS functions.
Additionally, the Director leads the selection, implementation, and ongoing management of the technology stack that underpins enterprise AI governance. The Director will work closely with product owners, enterprise IT, data engineering, clinical informatics, cybersecurity, and vendor teams as applicable. Reporting to the Senior Director, AIA, the Director of AI Technology bridges the worlds of data engineering, MLOps, and policy compliance, ensuring that governance is not a manual, after\-the\-fact exercise but an automated, embedded capability woven into how AI is built, deployed, and sustained across the organization.
Key Responsibilities
- AI Strategy \& Program Leadership: Lead development and execution of enterprise AI strategy and multi\-year roadmaps aligned with organizational goals.
- Portfolio Oversight: Oversee intake, prioritization, and sequencing of AI initiatives; balance value, feasibility, risk, and capacity across portfolios.
- Cross‑Functional Leadership: Partner with clinical, operational, technical, legal, compliance, and data leaders to advance AI initiatives from concept through deployment.
- Governance \& Responsible AI: Ensure AI initiatives adhere to governance frameworks, ethical standards, regulatory requirements, and risk management practices.
- Operational Enablement: Establish operating rhythms, performance metrics, and reporting mechanisms to track progress, outcomes, and value realization.
- Stakeholder Engagement: Serve as a trusted advisor to senior leaders; communicate AI strategy, progress, and impact through executive‑level updates and presentations.
- People \& Team Leadership: Lead, develop, and support managers and senior professionals; foster a culture of accountability, collaboration, and continuous improvement.
- Financial \& Resource Stewardship: Support budget planning, resource allocation, and business case development for AI initiatives.
Core Competencies
- Enterprise AI \& Digital Strategy
- Executive Presence \& Influence
- Change Leadership \& Resilience
- Ethical \& Responsible AI Stewardship
- Cross‑Functional Collaboration
- Program \& Portfolio Management
This is a hybrid position and incumbent must live within 100 miles of a Mayo Clinic campus. Fulltime remote may be considered.
Mayo Clinic will not sponsor or transfer visas for this position including F1 OPT STEM.
Qualifications Position Qualifications
- Bachelor’s degree and 10 years of progressive experience in healthcare, technology, operations, analytics, or related fields with demonstrated leadership responsibility required.
- Master’s degree and 10 years of progressive experience in healthcare, technology, operations, analytics, or related fields with demonstrated leadership responsibility preferred.
- Demonstrated experience leading high performing teams and delivering complex enterprise initiatives in a matrixed organizations preferred.
Exemption Status
Exempt
Compensation Detail
$204,256\.00 \- $306,384\.00 / year
Benefits Eligible
Yes
Schedule
Full Time
Hours/Pay Period
80
Schedule Details
Monday \- Friday, 8am \- 5pm
International Assignment
No
Site Description
Just as our reputation has spread beyond our Minnesota roots, so have our locations. Today, our employees are located at our three major campuses in Phoenix/Scottsdale, Arizona, Jacksonville, Florida, Rochester, Minnesota, and at Mayo Clinic Health System campuses throughout Midwestern communities, and at our international locations. Each Mayo Clinic location is a special place where our employees thrive in both their work and personal lives. Learn more about what each unique Mayo Clinic campus has to offer, and where your best fit is. Equal Opportunity
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, protected veteran status or disability status. Learn more about the "EOE is the Law". Mayo Clinic participates in E\-Verify and may provide the Social Security Administration and, if necessary, the Department of Homeland Security with information from each new employee's Form I\-9 to confirm work authorization.
Recruiter
Ted Keefe
Salary Context
This $204K-$306K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Mayo Clinic, 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($255K) sits 38% above the category median. Disclosed range: $204K to $306K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Mayo Clinic AI Hiring
Mayo Clinic has 5 open AI roles right now. They're hiring across AI/ML Engineer. Based in Rochester, MN, US. Compensation range: $148K - $372K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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