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About This Role
Primary Location Offices at Vernon Place
Department Anderson Center General Operations
Shift Day (United States of America)
Schedule Full time
Weekly Hours 40
FTE 1
Employee Status Regular
- Expected Starting Pay Range $91,520\.00 \- $116,688\.00
- Starting pay is based on experience, skills, and equity; exceptions may apply for highly qualified candidates. Additional pay (e.g., shift, on‑call, or weekend differentials) and benefits may apply. Annual pay may vary based on FTE status.
JOB RESPONSIBILITIES
Problem Analysis and Project Management\- Guide and inspire the organization about the potential and strategy of data science and artificial intelligence. Work with teams to understand, document and respond to requirements to meet moderate to highly complex analytic needs and identify data\-driven opportunities. Understand new data sources and process pipelines and catalog/document them. Determine requirements that will be used to train and evolve deep learning models and algorithms. Communicate and follow Data/AI Governance principles. Prioritize, scope, and manage data science projects and the corresponding key performance indicators (KPIs) for success. Execute own project tasks with urgency and to a high level of quality. Communicate status clearly and effectively using departmental project management tools. Follow time\-tracking and other project management requirements. Lead project meetings and workgroups.
Data Exploration and Preparation\- Create datasets using Enterprise data preparation tools. Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering, T\-distributed Stochastic Neighbor Embedding (t\-SNE), principal components analysis (PCA)Machine Learning to moderate to high complex questions. Generate hypotheses about the underlying mechanics of the business/clinical process. Test hypotheses using various quantitative methods. Proactively mine data warehouses to identify trends and patterns and generate insights for business units and senior leadership. Display drive and curiosity to understand the business/clinical process and collaborate with domain experts to better understand the system workflows and generation of data. Train other business and IT staff on basic data science principles and techniques.
Presentation \& Operationalization\- Prepare reports, presentations and visualizations to share insights with clinical, operational and research teams, using data display standards and best practice. Collaborate with data engineers, Xops teams and appropriate IT teams to automate and deploy solutions using established best practices,including source control and continuous monitoring. Integrate performance management and data quality tools into the current business infrastructure. Collaborate with other data science teams within the organization to evangelize and ensure compliance to organizational analytic practices and encourage reuse of artifacts. Collaborate with IT teams to inform Analytics production infrastructure processes.
Technical and Analytical Skill\- Develop mastery in IT skills in coding languages (Python, R, etc) and database programming languages for both relational and non relational data structures. Develop expertise in data wrangling, data mining, data \& quantitative analysis. Develop proficiency in distributed data/computing tools: MapReduce, Hadoop, HIVE etc. Become proficient in using the Enterprise Data tools as appropriate. Develop strong understanding of Xops methods to partner on data pipelines. Develop ability to communicate complex results to technical and non\-technical audiences, using story telling and creative visualizations. Continue to upskill through courses, local academia, networking etc. Ability to mentor \& coach junior level data science analysts.
Consulting\- Serves as a consultant to clinical, operation and research teams, regarding complex statistical \& analytical issues.
JOB QUALIFICATIONS
Master's degree in computer science, statistics, economics or related fields
5\+ years of work experience in a related job discipline
Preferred: 3\+ years of relevant project experience in successfully launching, planning AND executing data science projects
Preferred: Experience in other IT roles or functions such as quality assurance/testing, development, enterprise architecture, or project management. Demonstrated the ability to manage large data science projects and various teams
About Us
At Cincinnati Children’s, we come to work with one goal: to make children’s health better. We believe in a holistic team approach, both in caring for patients and their families, and in advancing science and discovery. We strive to do better and find energy and inspiration in our shared purpose. If you want to be the best you can be, you can do it at Cincinnati Children’s.
Cincinnati Children's is:
Recognized by U.S. News \& World Report as a top 10 best Children's Hospitals in the nation for more than 15 years
Consistently among the top 3 Children's Hospitals for National Institutes of Health (NIH) Funding
Recognized as one of America’s Best Large Employers (2025\) , America’s Best Employers for New Grads (2025\)
One of the nation's America’s Most Innovative Companies as noted by Fortune
Consistently certified as great place to work
A Leading Disability Employer as noted by the National Organization on Disability
Magnet® designated for the fourth consecutive time by the American Nurses Credentialing Center (ANCC)
We Embrace Innovation—Together. We believe in empowering our teams with the tools that help us work smarter and care better. That’s why we support the responsible use of artificial intelligence. By encouraging innovation, we’re creating space for new ideas, better outcomes, and a stronger future—for all of us.
Comprehensive job description provided upon request.
Cincinnati Children’s is proud to be an Equal Opportunity Employer committed to creating an environment of dignity and respect for all our employees, patients, and families. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, genetic information, national origin, sexual orientation, gender identity, disability or protected veteran status. EEO/Veteran/Disability
Salary Context
This $91K-$116K range is in the lower quartile 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 Cincinnati Children's Hospital, 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 ($104K) sits 52% below the category median. Disclosed range: $91K to $116K.
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.
Cincinnati Children's Hospital AI Hiring
Cincinnati Children's Hospital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cincinnati, OH, US. Compensation range: $116K - $116K.
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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