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About This Role
Join the Cleveland Clinic team where you will work alongside passionate caregivers and make a lasting, meaningful impact on patient care. Here, you will receive endless support and appreciation while building a rewarding career with one of the most respected healthcare organizations in the world.
As the HR Director – Digital, Data and Analytics, Information Technology, Artificial Intelligence and Cyber Security, partnering closely with executive technology leaders to shape workforce strategy, performance management, and talent practices across a highly distributed, technically specialized population. In this role, you will be the primary driver of culture and engagement. In addition, this role provides the Human Resource Director with an elevated level of autonomy while at the same time having a wide network of support and resources.
This opportunity allows you to be valued as a team member and supported as you achieve your goals. You will work alongside highly trained caregivers while building a rewarding career. This position will be in the office, but there is occasional work from home. This department allows caregivers to flex schedules to ensure work-life balance. The work culture in this department is positive and communication is clear between all team members.
A caregiver in this position works a hybrid schedule from 8:00 a.m. – 5:00 p.m.
A caregiver who excels in this role will:
- Serve as the strategic HR partner to Digital executive leadership, balancing organizational risk with business enablement through a “get to yes” mindset
- Lead and influence performance management, leader capability development, talent strategy, and workforce practices aligned to innovation and speed in competitive technical labor markets
- Coach leaders on realistic performance assessment, differentiation, accountability, and the resolution of complex, high-impact employee relations matters
- Provide oversight and guidance on complex investigations, compliance, risk mitigation, and creative workforce solutions, including non-traditional hiring locations
- Lead, develop, and partner with the HR Generalist supporting the group
- Create and ensure ongoing review of the HR strategic plan aligned to business goals and workforce planning needs of the area.
- Establish Objectives & Key Results (OKRs) to communicate, measure and track key outcomes of the strategic plan.
- Build and maintain relationships and partners with HR COEs to drive the Institute/Division/Hospital’s HR strategic plan and other HR initiatives, which may include workforce planning, diversity & inclusion, benefits, compensation, talent acquisition, workplace wellness, learning & engagement, HRIS & analytics, and HR communications.
- Direct and oversee the work of HR Services employees.
- Oversee that leaders are informed and compliant with organizational initiatives (e.g., annual performance reviews, merit, engagement, compliance, flu, etc.) and regulatory compliance.
- Maintains regulatory knowledge and expertise. Partners with the legal department as needed/required.
Minimum qualifications for the ideal future caregiver include:
- Bachelor’s degree in human resources, business, or related field.
- 8 years of progressive, related Human Resources experience inclusive of 3 years’ management experience and/or experience coaching and mentoring leaders.
- In-depth knowledge of employment and labor laws (EEO, FLSA, OSHA, FMLA, ADA, NLRA, etc.).
- Strong communications skills (verbal and written), group facilitation and presentation skills.
- Analytical skills in talent management, workforce planning, and HR strategy, including the ability to analyze and apply data measure performance and outcomes.
- Critical thinking skills, problem solving, decisive judgment and the ability to work with minimal supervision
- Employee relations experience
- Can build relationships at all levels of the organization.
- Proficient in Microsoft Office applications (i.e., Outlook, Word, Excel, and PowerPoint)
Preferred qualifications for the ideal future caregiver include:
- Master's degree.
- Human Resources certification (Professional in Human Resources (PHR), Senior Professional in Human Resources (SPHR), SHRM Certified Professional (SHRM-CP) or SHRM Senior Certified Professional (SHRM-SCP)
- Healthcare experience
- Experience supporting IT / Digital / Technology professionals
- Ability to have crucial conversations, implement strategic priorities, and think outside the box.
Physical Requirements:
- Ability to perform work in a stationary position for extended periods.
- Ability to travel throughout the hospital system and, in some locations, off-site.
- Ability to operate a computer and other office equipment.
- Ability to communicate and exchange accurate information.
Personal Protective Equipment:
- Follows standard precautions using personal protective equipment as required.
Pay Range
Minimum Annual Salary: $101,900.00
Maximum Annual Salary: $155,390.00
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 $101K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. This role's midpoint ($128K) sits 16% below the category median. Disclosed range: $101K to $155K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Cleveland Clinic AI Hiring
Cleveland Clinic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Beachwood, OH, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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