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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
AI/ML Engineers at Mayo Clinic play a pivotal role in the union of data, systems, and computer sciences. They work closely with a multidisciplinary team, including clinicians, user experience designers, product managers, IT professionals, and external partners, to develop and deploy effective, efficient, and ethical AI/ML solutions into clinical practice to enhance patient care and operational efficiency.
As a Senior AI/ML Engineer, you may work on the full spectrum of the AI life cycle from ideation to production. You understand the clinical environment well, including workflows, challenges, and requirements of healthcare providers and patients. You will leverage advanced techniques in AI/ML to analyze vast amounts of healthcare data, including patient records, medical imaging, and genomic information, to develop AI solutions that meet clinical needs and are integrated smoothly into clinical processes. You will develop, integrate, and standardize software components and create, maintain, and follow quality system procedures. You will guide the engineering of systems that are pivotal to developing and deploying these solutions, which encompass everything from design requirements, development, component creation, verification, non\-clinical validation, and risk mitigation to ensure our digital health technology products meet and exceed regulatory requirements and setting new benchmarks for safety and effectiveness in clinical settings. Your expertise will also extend to facilitating consistent and automated AI software solution development and releases through the design, testing, and maintenance of tools and associated CI/CD pipelines.
This role is instrumental in providing consultative services to departments and divisions, offering insights into complex business problems. Your ability to communicate complex findings in easily understandable terms to non\-technical users will bridge the gap between sophisticated AI technologies and clinical applications.
- Leading component design, development, integration, and standardization to create AI\-driven solutions that seamlessly integrate into clinical practice to enhance patient care and clinic operations.
- Collaborating with a multidisciplinary team, including clinicians, user experience designers, product managers, and IT professionals, to understand user needs, workflows, and clinical requirements and assess feasibility. Translating user feedback and requirements into design concepts and usability specifications for AI solutions.
- Leveraging machine learning techniques such as deep learning, natural language processing, computer vision, large language models, etc., to lead the design, development, and deployment of end\-to\-end AI solutions for healthcare applications.
- Establishing evaluation methodologies and performance metrics to assess AI solutions' effectiveness, usability, and impact in real\-world healthcare settings.
- Explaining data analysis results to guide strategic choices and clarify complex insights for non\-technical users to connect AI technologies and clinical applications.
- Overseeing the engineering of systems crucial for developing and deploying AI solutions.
- Facilitating consistent and automated AI software solution development and releases through the design, testing, and maintenance of tools and associated CI/CD pipelines.
- Contributing to implementing the best practices and standards for AI development and deployment methodologies, tools, and platforms.
- Providing mentorship, guidance, and technical leadership to junior engineers within the AI enablement team.
- Providing consultative services on areas of expertise to clinical work units or AI product teams, offering insights and strategies to address complex business problems.
- Providing training and education to healthcare staff on AI tools and technologies.
- Contributing to developing new AI methods and technologies that can advance the state\-of\-the\-art in healthcare AI.
This position is a combination of remote and on\-site work; individual must live within a 100 mile driving distance to the Arizona, Jacksonville or Rochester Mayo Clinic campus.
\*\*Visa sponsorship is not available for this position. Also, Mayo Clinic DOES NOT participate in the F\-1 STEM OPT extension program.
Qualifications* A master’s degree in engineering, computer science, mathematics, health science, or a related field with 4 years of experience, a bachelor’s degree with 6 years of experience.
- Extensive experience applying AI and machine learning in production healthcare environments or similar highly regulated or technology focused industries, showcasing an understanding of healthcare technology.
- Demonstrated leadership in managing complex projects, with a proven ability to navigate intricate project requirements and deliver successful outcomes.
- Proficiency in fostering collaboration across diverse teams and effectively communicating complex technical concepts to non\-technical stakeholders.
- Demonstrated expertise in cloud infrastructure environment and software development tools.
- Experience working with large, complex, and heterogeneous data sets, preferably in healthcare.
- Skilled in AI/ML techniques and frameworks.
- Familiarity with best practices in data engineering, data science, AI Engineering, and the MLOps communities.
- Demonstrated initiative in administration, education, software development, and technical reporting.
- A commitment to mentoring and training less\-experienced team members, coupled with strong interpersonal, communication, and time management skills.
Preferred Qualifications:
- A Ph.D. or other doctorate is preferred.
- Strong expertise in AI/ML techniques and frameworks, such as deep learning, natural language processing, and Generative AI, with proficiency in tools like Python, TensorFlow, PyTorch, sci\-kit\-learn, Keras, etc.
- Knowledge of the healthcare domain, including clinical workflows, electronic health records, medical terminologies, regulatory requirements, and industry standards.
- Familiarity with systems or quality engineering best practices, regulatory standards, and compliance frameworks, with the ability to adapt these effectively to different project scenarios.
- Demonstrated experience leading technical/quantitative teams in a regulated environment.
- Demonstrated experience creating risk management files and verification/validation strategies for digital health technology products within the healthcare industry.
- Strong expertise in user\-centered design, human factors engineering, usability testing methodologies, and evaluation across AI product development. Ability to conduct expert reviews using established usability practices and methods. Presents findings in easy\-to\-understand terms for the business or clinical practice.
Exemption Status
Exempt
Compensation Detail
$137,134\.40 \- $205,691\.20 / year
Benefits Eligible
Yes
Schedule
Full Time
Hours/Pay Period
80
Schedule Details
Monday \- Friday, 8:00 a.m. \- 5:00 p.m. (local time)
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
Pam Sivly
Salary Context
This $137K-$205K range is below the median 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 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 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($171K) sits 20% below the category median. Disclosed range: $137K to $205K.
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.
Mayo Clinic AI Hiring
Mayo Clinic has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Rochester, MN, US. Compensation range: $188K - $214K.
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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