Interested in this AI/ML Engineer role at Cleveland Clinic?
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Join the Cleveland Clinic team, where you will work alongside passionate caregivers and provide patient\-first healthcare. Cleveland Clinic is recognized as one of the top hospitals in the nation. At Cleveland Clinic, you will receive endless support and appreciation and build a rewarding career with one of the most respected healthcare organizations in the world.
As a Senior Advisor, you will lead executive\-level projects with a high level of complexity and organizational impact. In this role, you will serve as both a consultative resource and a partner in developing strategy for project execution. You will interact with external stakeholders on priority initiatives to deliver optimal results for the organization and be recognized as a highly specialized expert in one or more fields, such as nursing, medicine, or hospital operations, to clients and stakeholders. This position provides organizational support for the technically complex and evolving AI organization, with a strong focus on portfolio management to ensure organizational alignment and achievement of strategic goals critical to the success of the digital strategy.
A caregiver in this role works remotely in Ohio, Florida, or Nevada, Monday through Friday from 8:00 a.m. – 5:00 p.m.
*To be considered for this position, candidates must reside in Ohio, Florida, or Nevada.*
A caregiver who excels in this role will:
- Serve as a highly specialized expert to clients and stakeholders in identifying strategy and execution of high\-priority initiatives (e.g., revenue/reimbursement models, hospital operations) in both advisory and leadership capacities.
- Partner with C\-suite executives and key external stakeholders to develop sustainable practices that deliver optimal organizational outcomes, including hospital revenue models, by building strong relationships with external partners, regulators, and other key stakeholders.
- Act as a representative of the executive team in interactions with external stakeholders.
- Provide executive\-level leadership for additional organization\-wide or strategically significant initiatives as identified by the executive team.
- Support department and organizational initiatives as needed.
- Oversee hiring activities.
- Manage OKRs and ensure alignment with organizational goals.
- Organize and facilitate team meetings.
- Provide thought partnership, including development of presentations and speaking points.
- Mentor data scientists on organizational engagement.
- Mentor managers.
- Manage intake processes for the AI team.
- Coordinate external resources to drive execution of projects.
- Prioritize work across initiatives to ensure alignment with strategic goals.
- Support build\-versus\-partner decision\-making.
- Drive execution toward goals and deadlines.
- Other duties as assigned.
Minimum qualifications for the ideal future caregiver include:
- Bachelor’s Degree in Business, Healthcare or a related field
- 15 years of directly relevant experience including 5 years of previous leadership experience
- Master's degree *may* offset 2 years of required experience
- Critical thinking, decisive judgment and the ability to work with minimal supervision
- Ability to work in a stressful environment and take appropriate action
Preferred qualifications for the ideal future caregiver include:
- Master’s Degree
- Solid experience and track record in roles such as Chief of Staff, Vice President, or Senior Director in technical operations
- Experience in product development and the technology sector is *highly* desirable
- Several years of experience in strategy and operations roles (e.g. Chief of Staff, VP operations, technical or business operations executive, COO, etc.) with focus on driving execution at product focused technology companies
- Experience working directly with executives on leading complex cross\-functional technology initiatives
- Track record of developing operating models and execution tactics leading to operational efficiencies and faster development and deployment of products that use AI/ML technologies
- Experience working across diverse teams including AI scientists, engineers, program managers, and business executives and stakeholders
Personal Protective Equipment:
- Follows Standard Precautions using personal protective equipment as required for procedures.
Pay Range
Minimum Annual Salary: $151,220\.00
Maximum Annual Salary: $298,640\.00
The pay range displayed on this job posting reflects the anticipated range for new hires. A successful candidate’s actual compensation will be determined after taking factors into consideration such as the candidate’s work history, experience, skill set and education. The pay range displayed does not include any applicable pay practices (e.g., shift differentials, overtime, etc.). The pay range does not include the value of Cleveland Clinic’s benefits package (e.g., healthcare, dental and vision benefits, retirement savings account contributions, etc.).
Salary Context
This $151K-$298K 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 Cleveland 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 $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 ($224K) sits 5% above the category median. Disclosed range: $151K to $298K.
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.
Cleveland Clinic AI Hiring
Cleveland Clinic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cleveland, OH, US. Compensation range: $298K - $298K.
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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